DIRDC FREIGHT DATA
REQUIREMENTS STUDY
STAKEHOLDER
CONSULTATION FINAL
REPORT
A Research Report for the Department of Infrastructure, Regional Development and Cities
Dr Ronny Kutadinata, Stephanie Davy (ARRB); Rose Elphick-Darling (Deakin); DR Ali Ardeshiri, A/Prof Taha Hossein Rashidi (UNSW)
FINAL REPORT
28 February
2019
Contents
Abbreviations ........................................................................................................................................... i
1. Introduction ................................................................................................................................... 4
1.1. Methodology ......................................................................................................................... 4
1.2. About this report ................................................................................................................... 4
1.3. Key findings............................................................................................................................ 5
2. Literature review ............................................................................................................................. 9
2.1. Objectives, issues, and data needs ........................................................................................ 9
2.2. Understanding data ............................................................................................................. 17
2.3. Barriers for sharing data ...................................................................................................... 20
2.4. Other considerations ........................................................................................................... 21
2.5. Findings from the literature ................................................................................................ 22
3. Stakeholder consultation .............................................................................................................. 24
3.1. Interview consultation process ........................................................................................... 24
3.2. Online survey ....................................................................................................................... 28
3.3. Focus groups ........................................................................................................................ 32
3.4. Summary of findings ............................................................................................................ 35
4. Conclusions ................................................................................................................................... 38
4.1. Key findings.......................................................................................................................... 38
References ............................................................................................................................................ 45
Appendix A. Detailed online survey results .................................................................................... 49
A.1. Overview of survey respondents ......................................................................................... 49
A.2. Data requirements............................................................................................................... 65
A.3. Limitation & barriers to sharing freight data ..................................................................... 120
Appendix B. Best-worst scores ...................................................................................................... 127
Appendix C. Survey instrument ..................................................................................................... 143
Figures
Figure 2-1. Data sources, linkages and uses of an Australian TrSA ...................................................... 17
Figure 2-2. Illustration of data processing ............................................................................................ 18
Figure 4-1. An illustration of the key findings ....................................................................................... 38
Figure 4-2. What sort of entity are you responding on behalf of? ....................................................... 49
Figure 4-3. Entity role in the freight chain? .......................................................................................... 50
Figure 4-4. Please select which industry classification(s) best applies to your entity? ........................ 51
Figure 4-5. Please select which industry classification(s) best applies to your entity? ........................ 52
Figure 4-6. At which level is your entity involved? ............................................................................... 53
Figure 4-7. Small business entities annual turnover before tax ........................................................... 55
Figure 4-8. Medium business entities annual turnover before tax ...................................................... 55
Figure 4-9. Large business entities annual turnover before tax ........................................................... 56
Figure 4-10. Industry association annual turnover before tax ............................................................. 56
Figure 4-11. The primary type of cargo entities are involved with....................................................... 57
Figure 4-12. The second main type of cargo entities are involved with ............................................... 57
Figure 4-13. Please specify which commodity groups you work with .................................................. 59
Figure 4-16. Which mode of transport does your entity use to move the cargo? ............................... 64
Figure 4-17. Cross-tabulation of mode of transport & entity type ....................................................... 64
Figure 4-16. Cross-tabulation of mode of transport & the frequency of transport of goods .............. 65
Figure 4-17. Overal percent of data type sourced internally ................................................................ 67
Figure 4-18. Overal percent of data type sourced externally ............................................................... 91
Figure 4-21. Responses to the 6 propositions .................................................................................... 115
Figure 4-22. Are there any gaps in the currently available data sources required for your entity? .. 116
Figure 4-23. How important are the following transportation factors in moving freight more
efficiently?........................................................................................................................................... 121
Figure 4-24. In your opinion, which of the following items is the most important barrier and challenge
for freight data sharing? ..................................................................................................................... 122
Figure 4-25. Best Worst scores for all sample (n=148) ....................................................................... 128
Figure 4-26. Best-Worst Scores for Shippers (n=100) ......................................................................... 129
Figure 4-27. Best-Worst Scores for Receivers (n=95) ......................................................................... 131
Figure 4-28. Best-Worst Scores for Providers (n=104) ....................................................................... 133
Figure 4-29. Best-Worst Scores for Carriers (n=70) ............................................................................ 135
Figure 4-30. Best-Worst Scores for Small Business Entities (n=67) .................................................... 137
Figure 4-31. Best-Worst Scores for Medium Business Entities (n=37) ............................................... 139
Figure 4-32. Best-Worst Scores for Large Business Entities (n=25) .................................................... 141
Figure 4-33. Best-Worst Scores for Industry Association (n=10) ........................................................ 142
Tables
Table 3-1. Summary of online survey findings and focus groups ......................................................... 36
Table 4-1. Summary of findings of this study ....................................................................................... 42
Table 4-2. Participants’ employment size based on the entity type representing ............................... 54
Table 4-3. Participants’ employment size based on the Industry Association representing ................ 54
Table 4-4. What is the primary type of cargo your entity is involved with? * What is the second main
type of cargo your entity is involved with? Cross-tabulation ............................................................... 58
Table 4-5. What sort of entity are you responding on behalf of? * What is the primary type of cargo
your entity is involved with? Cross-tabulation ..................................................................................... 58
Table 4-6. Cross-tabulation between entity types and commodity groups, if the entity is a shipper of
goods ..................................................................................................................................................... 60
Table 4-7. Cross-tabulation between entity types and commodity groups, if the entity is a receiver of
goods ..................................................................................................................................................... 61
Table 4-8. Cross-tabulation between entity types and commodity groups, if the entity is a provider of
goods ..................................................................................................................................................... 62
Table 4-9. Cross-tabulation between entity types and commodity groups If the entity is a carrier of
goods ..................................................................................................................................................... 63
Table 4-10. Data sourced internally and its combination ..................................................................... 65
Table 4-11. Composition of data type sourced internally .................................................................... 66
Table 4-12. Cross-tabulation between type of entity & data category sourced internally .................. 69
Table 4-13. Cross-tabulation between data category & subcategory sourced internally .................... 71
Table 4-14. Cross-tabulation between data category & purpose of use for data sourced internally .. 73
Table 4-15. Cross-tabulation between data category & if the data could be shared, for sourced
internally ............................................................................................................................................... 74
Table 4-16. Cross-tabulation between data category sourced internally & subcategory for SBEs ...... 75
Table 4-17. Cross-tabulation between data category sourced internally & purpose for SBEs ............. 76
Table 4-18. Cross-tabulation between data category sourced internally & if the data can be shared for
SBEs ....................................................................................................................................................... 77
Table 4-19. Cross-tabulation between data category sourced internally & subcategory for MBEs..... 79
Table 4-20. Cross-tabulation between data category sourced internally & purpose for MBEs ........... 81
Table 4-21. Cross-tabulation between data category sourced internally & if the data can be shared for
MBEs ..................................................................................................................................................... 82
Table 4-22. Cross-tabulation between data category sourced internally & subcategory for LBEs ...... 83
Table 4-23. Cross-tabulation between data category sourced internally & purpose for LBEs ............. 84
Table 4-24. Data category (Internal) * Can this data be shared (Internal) Cross-tabulation – LBEs .... 86
Table 4-25. Cross-tabulation between data category sourced internally & subcategory - IAs ............ 87
Table 4-26. Cross-tabulation between data category sourced internally & purpose - IAs ................... 88
Table 4-27. Cross-tabulation between data category sourced internally & if the data can be shared -
IAs .......................................................................................................................................................... 89
Table 4-28. Data sourced externally and its combination .................................................................... 90
Table 4-29. Composition of data type sourced externally .................................................................... 90
Table 4-30. Cross-tabulation between the type of entity & data category sourced externally ........... 92
i
Table 4-31. Cross-tabulation between data category & subcategory sourced externally ................... 94
Table 4-32. Cross-tabulation between data category & purpose of use for data sourced externally . 96
Table 4-33. Cross-tabulation between data category & the frequency of used, for sourced externally
.............................................................................................................................................................. 97
Table 4-34. Cross-tabulation between data category & the cost to access, for sourced externally .... 98
Table 4-35. Cross-tabulation between data category sourced externally & subcategory, for SBEs .... 99
Table 4-36. Cross-tabulation between data category sourced externally & purpose for SBEs .......... 101
Table 4-37. Cross-tabulation between data category sourced externally & frequency of use, for SBEs
............................................................................................................................................................ 102
Table 4-38. Cross-tabulation between data category sourced externally & cost of access, for SBEs 104
Table 4-39. Cross-tabulation between data category sourced externally & subcategory, for MBEs . 105
Table 4-40. Cross-tabulation between data category sourced externally & purpose, for MBEs ....... 106
Table 4-41. Cross-tabulation between data category sourced externally & frequency of use, for MBEs
............................................................................................................................................................ 106
Table 4-42. Cross-tabulation between data category sourced externally & cost of access, for MBEs
............................................................................................................................................................ 107
Table 4-43. Cross-tabulation between data category sourced externally & subcategory, LBEs ........ 108
Table 4-44. Cross-tabulation between data category sourced externally & purpose, LBEs ............... 110
Table 4-45. Cross-tabulation between data category sourced externally & frequency of use, LBEs . 110
Table 4-46. Cross-tabulation between data category sourced externally & cost of access, LBEs ...... 111
Table 4-47. Cross-tabulation between data category sourced externally & subcategory, IAs ........... 112
Table 4-48. Cross-tabulation between data category sourced externally & purpose, IAs ................. 113
Table 4-49. Cross-tabulation between data category sourced externally & frequency of use, IAs ... 113
Table 4-50. Cross-tabulation between data category sourced externally & cost of access, IAs ........ 114
Table 4-51. Different combination of selection of proposition among the respondents................... 115
Table 4-52. Cross-tabulation between the type of entity and if there are any gaps in the currently
available data sources required for your entity .................................................................................. 117
Table 4-53. Cross-tabulation between data category in demand and if there are any gaps in the
currently available data sources required for your entity .................................................................. 117
Table 4-54. Cross-tabulation between data sub-category in demand and if there are any gaps in the
currently available data sources required for your entity .................................................................. 118
Table 4-55. Cross-tabulation between purpose of data in demand and if there are any gaps in the
currently available data sources required for your entity .................................................................. 119
Table 4-56. Cross-tabulation between if there are any gaps in the currently available data sources
required for your entity & the six propositions .................................................................................. 119
Table 4-57. Cross-tabulation of data categories in demand and the six propositions ....................... 120
Table 4-58. Important categories and sub-categories considered as a barrier for data sharing ....... 123
Table 4-59. Ranking of most to least important factor that participants (based on their role in the
freight chain supply) consider as a barrier to sharing freight data ..................................................... 124
Table 4-60. Ranking of most to least important factors that participants (based on their entity size)
had consider as a barrier to sharing freight data................................................................................ 125
Table 4-61. Cross-tabulation between the type of entity and if their entity is currently involved in any
existing cooperation between Australian data holders ...................................................................... 126
i
Abbreviations
ABS Australian Bureau of Statistics
ACCC Australian Competition and Consumer Commission
AGIMO Australian Government Information Management Office
AGLDWG Australian Government Linked Data Working Group
AIBE Australian Institute for Business and Economics (UQ)
ALC Australian Logistics Council
ANAO Australian National Audit Office
ANDS Australian National Data Service
API Application Programming Interface
APP Australian Privacy Principle
ARC Australian Research Council
ARRB Australian Road Research Board
ASAC Australian Statistics Advisory Council
ATDAN Australian Transport Data Action Network
AURIN Australian Urban Research Infrastructure Network
BITRE Bureau of Infrastructure, Transport and Regional Economics
CAV Connected and Automated Vehicles
CBA Cost Benefit Analysis
CITS Co-operative ITS
COAG Council of Australian Governments
CRC Cooperative Research Centre
CSCL Centre for Supply Chain and Logistics
CSIRO Commonwealth Scientific and Industrial Research Organisation
DIRDC Department of Infrastructure, Regional Development and Cities
DFAT Department of Foreign Affairs and Trade
DPMC Department of Prime Minister and Cabinet
ii
DTA Digital Transformation Agency
DTO Digital Transformation Office
FMS Freight Movements Survey
FOI Freedom of Information
GIF Graphics Interchange Format
GIS Geographic Information System
G-NAF Geocoded National Address File
GPS Global Positioning System
GVA Gross value added
HILDA Household, Income and Labour Dynamics Australia
IAP Intelligent Access Program
ICT Information and Communications Technology
IDI Integrated Data Infrastructure
iMOVE iMOVE Australia (incorporating the iMOVE Co-operative Research Centre)
LBE Large business enterprise
IoT Internet of Things
IP Internet Protocol
IPA Infrastructure Partnerships Australia
IT Information Technology
ITS Intelligent Transportation Systems
JSON JavaScript Object Notation
MaaS Mobility as a Service
MADIP Multi-Agency Data Integration Project
MBE Medium business enterprises
MOG Machinery of Government
MOU Memorandum of Understanding
NCRIS National Collaborative Research Infrastructure Strategy
NDC National Data Custodian
iii
NFSC National Freight and Supply Chain (Strategy)
NID National Interest Dataset
NHVR National Heavy Vehicle Regulator
NSS National Statistical Service
NTC National Transport Commission
NSW DAC New South Wales Data Analytics Centre
OAIC Office of the Australian Information Commissioner
OECD Organisation for Economic Co-operation and Development
PC Productivity Commission
rCITI Research Centre for Integrated Transport Innovation (UNSW)
SBE Small business enterprises (LBEs)
SMART SMART Infrastructure Facility, University of Wollongong
SMVU Survey of Motor Vehicle Use
TCA Transport Certification Australia
TIC Transport and Infrastructure Council
TfNSW Transport for New South Wales
TMR Department of Transport and Main Roads Queensland
4
1. Introduction
This report presents the analysis and findings from the stakeholder consultation segment of the FDRS,
trying to better understand the information needs of the many stakeholders in both the public and
private sectors of the freight and supply chain sector.
1.1. Methodology
The stakeholder consultation was undertaken in two stages, as follows:
• First, a targeted literature review was conducted to review relevant government and industry
reports, particularly the various literature supporting the National Freight and Supply Chain
Strategy. The focus of this review was to understand what had been said and done.
• Second, a survey of stakeholders was undertaken. This survey used a mix of methodologies
suited to the compressed timeframe. This allowed the project team to execute these surveys
concurrently to achieve complete coverage in a short timeframe
1.1.1. Survey method
The survey process utilised three forms of engagement.
The most widely deployed method was an online survey which was applied through a stratified
sampling methodology that ensured adequate responses were received from all stakeholder groups.
On-line surveying suits time poor respondents by using close-ended response modes, but is
necessarily limited in the depth to which it can inquire. The study received 148 completed responses.
The second method was direct interviewing of key respondents selected for the depth of their
knowledge of the subject matter (within the scope of their organisation). Telephone interviews
generally deliver more direct and focused responses compared to other means and enable more open-
ended questions than can be achieved through an online survey. A total of 37 interviews were
conducted.
The third process was the conduct of focus groups. These enabled a deeper qualitative analysis of
some issues and also enabled interim observations gleaned from the survey process to be tested and
refined. Three focus groups were held.
By applying a mix of survey methodologies, this study was able to derive a wide range of information
from multiple sources and able to identify and define the widely varying preferences and needs of
stakeholders.
1.2. About this report
This report is structured as follows:
• Section 2 describes the main results of a focussed literature review;
5
• Section 3 describes the results of the stakeholder consultation, including the:
- Telephone interviews;
- Surveys; and
- Focus Groups; and
• Section 4 draws together our main conclusions.
Appendices A and B provide detailed results of the survey. Appendix C describes the survey
instrument (i.e. the questionnaire).
1.3. Key findings
1.3.1. Main themes
In discussions with stakeholders regarding their data needs and priorities, three key themes were
identified:
• What, where, when and how much? There is strong demand for a more complete picture of
what goods (bulk, non-bulk, containers) are being moved where and when across the
transport network because of the potential savings in cost and time from improved decision-
making.
• Appropriate transparency and aggregation. A key trade-off is that the provision of data needs
to be suitably transparent to enable benchmarking whilst also aggregated enough to
accommodate commercial sensitivity.
• Data exchange needs to offer mutually beneficial outcomes. An emphasis on the potential
usefulness of outputs is necessary to encourage improved data sharing.
1.3.2. Performance metrics: movements, cost, time, and capacity
The fundamental need expressed by most stakeholders is to learn about the performance and
competitiveness of some aspect of the national supply chain. The metrics sought depend on the
stakeholders’ interests and the scope of the decisions they are seeking to support. However, the
underlying data that serve this purpose relate to four aspects:
• goods movements (“what, where, when, and how much”),
• associated costs,
• time (i.e. service level and reliability), and
• capacity (i.e. utilisation, congestion, and infrastructure conditions).
6
The consultation process revealed that stakeholders prioritise data on cost and volume (freight task)
ahead of the other aspects. However, some other contextual datasets, such as infrastructure condition
data and employment data are also frequently sought.
Our review of previous reports revealed the importance of economic competitiveness (productivity,
efficiency, and reliability). This study, particularly from online survey, reinforced this view. We found
that business entities, particularly small business entities, commonly seek insights into the
competitiveness of their operation, whereas governments, larger firms and industry associations are
more concerned about planning and investment decision-making.
In addition to this attention to economic competitiveness, the study also identified the importance of
end-to-end network visibility, which enables decision makers to identify problems (eg. bottlenecks)
and reduce waste of time and effort, in supply chains.
The study also identified the importance of: nationally significant freight corridors; first/last-mile
deliveries; urban freight; gateways; capacity management; and data requirements for modelling
purposes.
1.3.3. Interdependent relationships
It has been observed that industry, state, federal, and local government stakeholders are partners in,
an interdependent relationship, in the sense that there is an inter dependence (and shared
responsibility) between government and industry to fulfil freight data needs. Governments have an
obligation to manage the transport networks, which are used by the freight industry but only the
freight industry can report the use they actually make of those networks. Freight data typically has
both ‘private’ and ‘public good’ value. The challenge is to find ways by which the government can
invest in collecting and collating privately held data to generate public value without destroying the
private value of that data in the process.
To do this, greater trust needs to be created between the government and the industry. To facilitate
this, there may be a need for a neutral entity that can take responsibility for undertaking data pre-
processing steps and data aggregation (to ensure commercial confidentiality) before distributing it for
other stakeholders to use.
1.3.4. Transparency on benefits
The industry has shared their concerns on data sharing in several fora including in submissions to
major recent public inquiries. In general, they are not opposed to sharing their operational data to
help improve the efficiency and productivity of supply chains.
Despite being willing to share their data, the industry was reluctant to make commitments and/or
undertake new initiatives. This is mainly due to industry uncertainty around the benefits they would
derive in return for the effort they must make to share their data. Industry expressed scepticism about
the value they have received to date from their data sharing in the past. Some of the concerns
expressed were:
• Lack of timeliness on datasets delivery/dissemination;
7
• Lack of systematic data collection;
• Lack of end-to-end visibility due to fragmented datasets; and
• Lack of traction from previous initiatives on establishing some sort of ‘data centre’.
Participants also indicated:
• They would be unwilling to share commercially sensitive data; and
• They sought that the effort and cost to them of additional data collection and processing (for
sharing purposes) should be either minimal or funded by government. Alternatively, they
welcomed the prospect of low-cost automated processes. This view was strong among
smaller business entities, but less of an issue for larger businesses.
1.3.5. Learning from existing datasets
The study also identified several existing programs and associated datasets and tools that are
considered to be particularly useful. These include: BITRE yearbook, ABS surveys (Motor Vehicle Use
and Freight Movement), CSIRO’s TraNSIT and TfNSW Freight Performance Dashboard.
However, it was frequently commented that the available data is lacking in one respect or another.
Common observations were that:
• data updates are too infrequent,
• timeliness of delivery is often lacking, and
• the level of aggregation and presentation of the datasets is not suitable for the needs of the
users.
1.3.6. Datasets in greatest demand
The study has clearly identified several datasets that are needed by stakeholders:
• Most notably, freight movement data (at various granularity levels); and,
• more broadly, performance indicators of the supply chains; particularly cost and time
components of goods movement. Costs, service levels, and reliability are the most typically
used measures of performance.
Segments of supply chains that were identified as needing greater clarity are:
• urban freight;
• first/last mile;
• regional issues;
• gateways;
• nationally significant corridors; and
8
• issues related to some specific commodities.
Respondents commented that the eventual goal is to achieve holistic freight data coverage in order
to provide end-to-end visibility for the decision makers.
1.3.7. Better coordination is required
The literature review and stakeholder responses suggest that the deficiencies associated with
currently available datasets stem more from collection procedures and information
delivery/dissemination rather than the subject matter being collected. It appears that there are more
issues associated with the ‘how it is being collected and disseminated’ than with the ‘what is being
collected’.
9
2. Literature review
This section presents the findings from the literature review.
2.1. Objectives, issues, and data needs
The section summarises the objectives, issues and underlying needs driving the demand for data. Data
needs can be classified into several themes as follows (Taniguchi & Thompson 2015, CISCO 2018).
2.1.1. Economic competitiveness: productivity, efficiency, and reliability
Australia’s freight supply chain is a vital economic cog and key strategic asset. The overall performance
of Australia’s supply chain impacts on achieving higher productivity growth and raising living
standards. The three aspects of this broad theme, namely productivity, efficiency and reliability, are
clearly interlinked and inseparable. Arguably, this is the main driving factor in relation to improving
data collection for supply chains (TfNSW 2018, TfV 2018, IPA 2018, DIRDC 2018a, ALC 2018, Austroads
2006, Australian Railway Association & IISRI 2018, TMR 2013, Heaney 2013).
There are several key components in this theme, including:
• costs;
• capacity utilisation;
• data from trials of new technology;
• travel times, service times and reliability (congestion);
• freight growth management;
• land and corridor protection for freight;
• infrastructure performance;
• use of more productive and efficient vehicles;
• first/last-mile issue;
• border issues;
• end-to-end visibility (understanding where the pinch points, bottlenecks, constraints, and
breakdowns are across the supply chain);
• regulatory or governance problems; and
• performance of gateways.
These identified components traverse the three levels of decision making defined in the scope of this
study, namely: operation, planning, and investment.
10
Additional issues were identified by DIRDC in its “Inquiry into national freight and supply chain
priorities” report (2018a), as follows:
• capacity limits and land-side access restrictions at key national freight terminals;
• diminishing industrial land around key national freight terminals and an inadequate allocation
of land for intermodal terminals;
• conflicting freight and passenger rail and road movements during peak periods;
• fragmented access to national key freight routes;
• inadequate mechanisms for national supply chain integration, including a lack of freight data
and information on the performance of Australian supply chains against international
benchmarks;
• inadequate jurisdictional strategies for protecting freight corridors and strategic industrial and
logistics areas from urban encroachment; and
• a lack of integrated planning and harmonisation of freight regulation and coordinated freight
governance across and within governments.
These challenges may impose significant costs on freight businesses, Australian consumers and
exporters.
2.1.2. Safety
Another important consideration is safety. Both NSW and Victoria included in their respective freight
plans the intention to adopt new technologies and vehicles that may improve safety (TfNSW 2018, TfV
2018, TMR 2013). In this regard, data may play a part in informing which technology and vehicles
provides the best return on investments in terms of safety benefits.
Additionally, crash data can be (and is) utilised to determine accident “black spot”, which in turn can
be actioned by the relevant road operators to reduce the number of crashes (Meuleners et al. 2002,
Tziotis 1993).
Finally, safety improvements will inherently contribute to the economic competitiveness of the supply
chain industry. For instance, Budd & Newstead (2014) provided an estimation of the financial savings
associated with the uptake of more advanced vehicle safety features. For instance, the report
indicates that if Autonomous Emergency Braking Systems (AEBS) were to be equipped in all heavy
vehicles at all speeds, it would lead to a 25% fatal crash reduction with an estimated value of $62-187
million for Australia and $21-62 million for New Zealand. Furthermore, this translates to 67 and 14
lives saved in Australia and New Zealand respectively. Clearly, such safety-related data would help
decision makers to justify safety-related investments.
2.1.3. Environment and sustainability
Environmental and sustainability considerations are also a focus of the literature as issues that need
attention.
11
For example, noise TfNSW (2018) has pointed out that noise emissions around airports and rail freight
supply-chains needs to be carefully managed. Additionally, noise emissions have been identified as a
potential problem for proposals supporting off-peak freight delivery (Holguín-Veras et al. 2014,
Austroads 2016, 2018a).
Other than noise, fuel emissions and the health impacts of heavy vehicles are identified as important
considerations in the NSW Freight Plan (TfNSW 2018).
These sustainability considerations are intimately linked to supply-chain efficiency as well as freight
corridor reservation.
2.1.4. Infrastructure and management
Infrastructure plays an important role in ensuring the efficiency of the freight supply chain network
and is, therefore, an important aspect of the literature (TfNSW 2018, DIRDC 2018a, 2018b, TfV 2018,
IPA 2018, ALC 2018, Austroads 2006).
Data about conditions of infrastructure and assets would improve the prioritisation and management
of maintenance, operation (ie. avoiding bottlenecks), and congestion management, applicable to all
modes (road, rail, sea, air). This is an area where new technologies developed in recent years have
permitted data to be gathered and transmitted in real-time.
2.1.5. Interaction with structures
As part of the operation of freight vehicles, it is important to ensure that the roads and other
structures (such as bridges) can accommodate the sizes and length of such heavy vehicles. The
Victorian Freight Plan (TfV 2018) prioritised updating the principal freight network, as well as
expanding the high productivity freight vehicle network. Further, the Plan identified the importance
of developing freight friendly solutions for the Melbourne CBD. As another example, TfNSW (2018)
has indicated the importance of protecting land needed for vital freight and logistics operations.
2.1.6. Modelling and forecasting
Modelling and forecasting have been identified as important exercises that help inform decision
makers about the future challenges of various aspects, eg. policy, infrastructure provisions, economic
impact, predictive congestion management, vehicle impact on transport network (BITRE 2018e, KPMG
& Arup 2017, ALC & ACIL Allen Consulting 2014, SBEnrc 2017, Austroads 2018c, 2014, 2011, DG Cities
2018). More specifically, several researchers (Hensher et al. 2018, Camargo & Walker 2017) have
provided various methodologies to analyse freight movements with the help of data.
Additionally, the TranSIT model which has been developed by CSIRO (2018), utilises data from the
agriculture supply chain and serves as a strategic investment tool, which may help identify the most
cost-effective options of infrastructure investments. Finally, Austroads (2006) has also pointed out
that commodity-based modelling is preferred to vehicle-based modelling. This may have implications
on the data requirement of developing the model.
12
2.1.7. Identified data needs
Based on the above, some of the data needs have been identified from the literature. A more
expanded discussion of data needs can be found in the WP2 report.
2.1.8. Performance measures
Performance measures have been identified as the key data that are required to improve the overall
performance of the supply chain industry.
DIRDC (2018a, 2018b) has emphasised the importance of measuring and monitoring the performance
of supply chain such that actions can be taken that will improve productivity, as well as informing
capital investments, maintenance, regulatory and governance reform. It also emphasises the needs of
data consistency across jurisdictions. Although in some ways largely self-evident, the complicated
structure of the supply chain, with different agents acting as owners and operators for example, makes
it much less likely that there is a natural incentive for a particular stakeholder to collect these kinds of
datasets.
There were many examples of performance indicators identified in the literature, particularly in the
extensive logistics and operations research literature (TfNSW 2018, TfV 2018, Australian Railway
Association & IISRI 2018, Katsikides n.d., KPMG 2018, NTC 2016b), including:
• rail terminal utilisation;
• rail service reliability and punctuality;
• road-to-rail ratio;
• truck service reliability and punctuality;
• truck queue time;
• truck two-way loading ratio;
• truck and booking slot utilisation;
• truck and container turnaround time;
• movement of cargo from/to port by rail (eg. port botany);
• location tracking and condition data, such as temperature and care when handling;
• freight movement: speeds, travel time, reliability, truck volumes, significant locations and
corridors, o-d, route diversions;
• cost per tonne kilometre;
• total cost per tonne of the supply chain freight task;
• total time taken per supply route;
13
• a unitised measure of time (such as tonnes shipper per day); and
• tonnes moved per driver/per vehicle.
The performance indicators identified in the literature not only cover financial aspects of supply chain
performance such as cost, but also asset performance and service quality (time, reliability). For
example, Austroads (2018b) differentiated performance indicators into three different types: assets,
finance, and service.
2.1.9. Externalities
An externality is an economic term that describes a policy, decision, action or institutional framework
that leads to an impact outside the control of the entity in question. For example, freight companies
are affected by urban traffic congestion, which is caused by an imbalance in the demand and supply
of road space (which is shared by private, public and freight vehicles). There is nothing an individual
freight company can do about congestion – it’s an externality beyond its control.
The literature identifies several externalities (and available data) that will influence decision making
within the freight supply chain industry. Examples of this type of data includes:
• congestion data;
• environmental impact data;
• employment data;
• licensing data;
• customs data (NTC 2017); and
• data on the supply of land for industrial uses (eg. Greater Sydney in NSW Freight Dashboard
(TfNSW 2018)).
2.1.10. Data gaps
The issue of data gaps has been mentioned numerous times in the available literature. Austroads
(2006) argued that:
It is interesting to compare this statement with one made recently by IPA (2018), as follows:
“At the same time every freight inquiry in the last 25 years and most of the stakeholders
consulted in this study identified the need for better data quality and quantity. They identified
problems with current collections: such as the level of geographic disaggregation available from
both the ABS Survey of Motor Vehicle Use (SMVU) and FDF Freight Info data and general
collection quality and comparability. However these were far outweighed by concern about lack
of collections. There was a lack of specific data: for example, there are few rail data post
privatisation and a general dearth of data at many levels.”
14
While raw data collection has increased since Austroads made its observation in 2006, the issue of the
data being isolated and fragmented remains as recently pointed out by IPA. Austroads (2018b) has
recently highlighted the issue of fragmented data.
A key outcome of the gap assessment presented in the report identified the following gaps:
• the lack of a consistent implementation of a data standard to support the knowledge sharing
framework;
• the lack of assessment of data quality and maturity across agencies;
• there are no defined, agreed or consistent data processes, including data collection and the
standardisation of spatial data;
• there are no established benchmarking requirements for agencies and jurisdictions to
reference; and
• evidence-based decision making is not a consistent, understood priority for road management
in Australia and New Zealand, although recent governance changes in Australia (and plans in
NZ) have in part addressed this issue.
In addition to the general issue above, NTC (2016a) and ABS (2011) have identified the following more
specific data gaps:
• the number of ancillaries versus hire-and-reward vehicles involved in road freight;
• the number of employees per fleet involved in road freight;
• the volume of commodities moved on rail freight networks;
• freight rail network utilisation;
• the fleet profile for tourist train operators;
• tourist rail usage;
• passenger rail network utilisation; and
• detailed, up-to-date economic measures of transport activity undertaken within the
Australian economy that separately identify the own-account transport activity of businesses
operating in industries.
“Our work shows that the freight data deficit is not due to a lack of data collection. Much of the
data decision makers need is already collected, but it remains fragmented, in silos, and rarely
analysed. We have found systematic collection and publication of information about network
performance is routinely deficient – often held in a patchwork of isolated datasets spread across
tiers of government, industry, and the supply chain.”
15
2.1.11. Transport satellite account
The ABS (2011) has proposed the use of an Australian Transport Economic Account, an experimental
Transport Satellite Account (TrSA) that provides a more comprehensive picture of transport by
bringing together components of transport activity throughout the Australian economy. The
development of a TrSA would provide data critical to supporting evidence-based decision making in
the transport industry. A TrSA has the potential to assist in answering key policy questions such as:
• the economic impact of transport policies (eg. road user pricing, congestion charges, fuel
surcharges) on all Industries, final consumers and the economy as a whole; and
• better understanding of broader transport activity in the economy including employment,
productivity, energy consumption and the environment.
NTC (2017) suggested that any TrSA would include the following:
• the contribution of for-hire transport and own-account transport activity to industry gross
value-added and GDP (among other aggregates);
• own-account transport would be treated as a single industry and valued based on the cost of
its inputs;
• data may be split by passenger/freight activity and modal data (air, road, rail, water) but not
by vehicle type;
• options to estimate profits on own-account transport would be explored;
• transport volume data (that is, number of vehicles) would be subject to quality of the data;
• capital expenditure data by vehicle type may be restricted to road vehicles and all other
vehicles; and
• estimates of transport employment and hours worked would be explored.
Figure 2-1 below outlines in detail the linkages between various data sources which would support a
TrSA, the national accounting framework and specific uses of TrSAs. The data requirements for a TrSA
would encompass:
• transport related inputs (expenditure) data:
- own-account transportation output;
- a range of additional financial and some non-financial data as captured in the 2010-11
Economic Activity Survey, from both the Transport industry and in terms of transport
activity undertaken in all other Industries;
- transport related operating expenses (inputs) for each mode;
- broader level transport expenses by mode from non-transport industries;
16
• production of transport services (income) data:
- income from transportation services and its details (yet own-account transport activities
are not able to be separately identified on the income side); and
• additional data requirements:
- transport physical or volume data (for each industry), such as the number of transport
vehicles and distance travelled classified by type of transport vehicle (eg. trucks, buses,
cars, trains etc.); and
- Transportation employment data, including employment aggregates and employee
characteristics, the value-added ratio (ratio of own-account transportation value-added
to total value added for each industry) to numbers of employees in each industry; and
wages data/labour force ratios.
17
Figure 2-1. Data sources, linkages and uses of an Australian TrSA
2.2. Understanding data
The literature review highlighted that data necessarily comes in different formats and types. Thus, it
is important to understand the form of the data that is most useful to industry.
2.2.1. Data processing
As first proposed by Keever & Pol (2002), there are four levels of data processing, as follows:
• Level 1: Data object refinements. At this level, data objects are refined into a consistent set of
units. The data objects may be collected from various data collection procedures.
• Level 2: Situation refinements. The data from Level 1 is interpreted into meaning, similar to
how human interpret the meaning of sensor data.
18
• Level 3: Expectation refinements. The current situation is extrapolated into the future (ie.
forecast).
• Level 4: Meta process refinements. This provides a feedback loop that helps improving the
overall process.
Based on the four levels of data processing described above, the output of each level of refinement is
in essence a different type of dataset, which will be of different types and formats, compared to the
inputs into the level. These datasets may address the same issue/objective yet might be of different
scope. For example, speed data from loop detector may indicate a significant drop in speed, which is
useful for an operation perspective to minimise risk of incidents. Further, the situation refinement
process would interpret this as a potential incident data object, which is potentially used for planning
purposes (eg. safety management plan). The potential incident data then can be forecasted to help
prioritise road upgrade projects (investment) to increase safety. This example highlights the
importance of the different types of data based on the refinement levels.
The image below also describes a similar concept. It shows that data objects may undergo some
processing before being delivered to the users.
Figure 2-2. Illustration of data processing
2.2.2. Data quality
Furthermore, it is important to note the importance of so-called ‘data quality’. ISO (2008) has
defined data quality as follows:
19
Several reports have suggested that data utilisation and sharing is lacking due to the fragmented
nature of the data and emphasise the importance of consistency and standardisation (Austroads 2006,
2015, Ueda 2017, ALC 2018, Productivity Commission 2017, ACS 2017, NTC 2017, ITF 2015, IPA 2018,
TIC 2016).
The concept of high value datasets was discussed by the Productivity Commission (PC 2017), which
has two components, namely use and quality. The PC identifies several characteristics around use that
high value datasets might possess, include that they (PC 2017, p.288):
• are unique (in the sense that there are no suitable substitutes or that they could not be easily
replicated);
• contain unit record level data (which can be particularly useful for evaluating the effectiveness
of particular policies);
• have a high degree of coverage in the population of interest — which minimises issues around
sampling bias and allows for analysis of small and vulnerable groups;
• have been designed for linking with other datasets, or use identifiers to allow linking with
other datasets;
• are central to service delivery and/or core decision making;
• contain time-specific data that allows for comparisons to be made over time; and
• have a high potential for use and re-use, and a large potential user base.
Characteristics that are indicative of quality could include that datasets:
• are current (real-time) and/or updated regularly;
• are accurate and complete;
• contain clear, consistent definitions; and
“Data quality is a slight misnomer since the “perception of quality” or “measurement of
excellence” is not what we really mean here. These terms actually relate to the perception of
quality by the data consumer and are terms used to assess the fitness for purpose of the
received data. What we mean in this Technical Report by the term “data quality” is a set of
meta-data which defines parameters relating to the supplied data or service that allows data
consumers to make their own assessment as to whether the data is fit for their intended
application. Different applications require different aspects of data quality and so it is not
possible to say, for instance, that a data set with a reporting interval of one minute is of a higher
quality than one with a reporting interval of 3 min. Only the data consumer can make this
judgement of “perceived quality” since it must be based on the needs of their application (eg. in
terms of timeliness, accuracy, completeness, etc.).”
20
• provide details on data quality, lineage and provenance.
2.2.3. Stakeholders
It is also important to consider ‘who’ among the stakeholders needs the data, since their data needs
may vary significantly, depending, for example, whether the stakeholder is a government or a private
sector entity. In addition to the entities that are directly involved within the supply chain, there are
several other stakeholders that are of relevance to this study. These stakeholders are important and
a critical part of the Australian freight supply chain eco-system, with their own unique challenges and
data needs:
• original equipment manufacturers, including:
- technology suppliers;
- vehicle manufacturers;
• peak industry bodies;
• research agencies; and
• government entities, including:
- regulators;
- local councils;
- road operators; and
- state/federal government departments.
2.3. Barriers for sharing data
Notwithstanding a general consensus about a lack of transport data and strong support for a national
freight data strategy, the literature review identified several factors that act as barriers to data sharing.
Austroads (2006) and the Productivity Commission (2017) offer the following list of these factors:
• There is a lack of consistency, transferability and standardisation of data collection
procedures. In many instances, legacy IT systems hinder automation of data provision.
• Issues of commercial confidentiality are important, since some of the stakeholders are
competitors at times and there will be data that they will not want to share. Commercial
confidentiality is perceived as an important issue, especially in rail and aviation.
• There is concern about to how much benefit, if any, individual organisations would derive
from data collaboration. Stakeholders almost unanimously said that the value of collaboration
would need to be well established and understood before they would support a collaborative
venture.
21
• In addition, many organisations in Australia note problems associated with the fragmented
nature of freight data and the cost involved in locating, accessing and using these data.
AusLink has highlighted the need for consistency between jurisdictional data sets to enable
national comparability.1 Other stakeholders have noted the fragmented nature of many
collections, and that sporadic releases detract from data usability.
• Stakeholders were also concerned about the balance of benefits and costs, particularly as
regards their own organisations. There was concern that benefits would likely be distributed
to business and the community, but that most of the costs from a formal freight data
collaboration system would be borne by contributing organisations. These could take the form
of opportunity costs of staff time in all levels of the organisations, from the time of senior
people reaching agreements in the planning stage, through infrastructure setup, to ongoing
operation.
• Finally, there are operational, legal and political risks to consider when data is shared with
other, perhaps competing, organisations and control is lost over data use and distribution.
There is considerable legislative complexity, as well as concerns about data breaches and re-
identification of individual contributors.
2.4. Other considerations
While the barriers to data sharing are considerable, there may be means of managing some of the
obstacles that have been identified (Austroads 2006).
The data sharing mechanism itself may not be as important, as long as there is a nationally consistent
system. Such a system would also be “useful for methodologies, generation rates and time trends
parameters”, as well as to “provide the level of detail required”. It is also needed “ahead of a national
freight data system to extend collection, transfer and to get the data needed at the level of
disaggregation suitable for use”.
In terms of governance, “a national freight data consortium may present a single client with greater
buying power to influence the content and manner of collection of privately-available data”. Such
collaboration “can be arranged via informal and formal agreements, MOUs, licensing agreements and
legislation”. As part of the coordination, representatives from the major contributing organisations
will form a governing body or steering committee. Furthermore, the operations of the data centre will
be the responsibility of existing agency (such as the primary government sponsoring body), or a third-
party data custodian (to address the “concern about state and national governments controlling
access to information”).
While government funding would need to be provided initially, once operating, a national data
collection initiative should be self-sustaining in the long term. For instance, products and services
could be made available to the general market at a cost (but be available free for partners). In this
1 At the same time, there may be opportunities to reduce the costs of replicating data surveys by translating data sets for an industry from one region to other regions, if consistent processes to do so were available.
22
way, ‘customers’ of the database could come from all sectors of government, industry and community
as well as the general public.
2.5. Findings from the literature
The literature review highlighted that there is already quite an amount data being collected, through
various government programs, eg. IAP (TCA 2018), ABS surveys (ABS 2005, 2015, 2017), BITRE statistics
(BITRE 2018a, 2018b, 2018c, 2018d), and container stevedoring monitoring reports (ACCC 2018). Yet
accessing and making use of the data is not necessarily straightforward:
• The available data is presented in an aggregated format, which may be more useful for
planning/investment purposes. This points to an important trade-off between data
aggregation, which may be useful from a government planning perspective, versus data
granularity, which may be more useful for firm-level planning.
• The main reasons why firms are reluctant to share data is that the benefit of doing so may be
uncertain or may not outweigh the perceived concerns (eg. commercial confidentiality). There
are also concerns that a government-run national data entity would ‘control’ what it wants to
share. There may therefore be a case for establishing a structurally independent data agency.
• The cost of locating and accessing data is also an issue, due to the non-standardised data and
the fragmented/siloed nature of current data collection.
Thus, it is important to ensure that the surveys be designed such that the stakeholders’ understanding
of data is addressed, including the types of data, what it is used for, as well as their willingness to share
data.
The following main findings relate to freight data needs and availability:
• The focus of governments is to improve national productivity and international
competitiveness. Further, there are several other important objectives including: safety,
infrastructure management, and modelling/forecasting for planning purposes.
• The data needs of the stakeholders are mainly driven by the desire to be able to understand
the performance of the supply chains, with an eventual goal to achieve end-to-end visibility.
• In this regard, datasets that are highly sought after include: congestion, travel time and asset
condition. Associated datasets include: employment, licensing and customs data.
It is important to note that the needs and interests of industry and government are not necessarily
aligned. While governments will generally adopt a broader perspective that is focused, for instance,
on the productivity or safety of an industry, individual firms can reasonably be expected to be focused
on their own performance and profitability. While these respective objectives may coincide in some
instances, there is no guarantee that this will always be the case. As stated by DIRDC (2018a):
23
In contrast, for industry, the most commonly sought data relates to performance metrics of the supply
chain. Typically, performance is measured in terms of utilisation, service level and reliability, cost, and
goods movement (volume, route, time).
Advances in data collection technology explain much of the renewed focus on freight data. For
instance, TfNSW (2018) when proposing actions to improve economic growth highlighted a need to
assist industry planning and decision making by sharing data with industry, improving data on rail
freight and supporting national freight data initiatives.
The literature identifies several other reasons for collecting data, including: safety, environmental
impact and sustainability, infrastructure and management, interaction with structures, and finally
modelling and forecasting. The needs identified from the literature mostly refer to planning and
investment decision making, while operational decision making was cited more infrequently.
Several externalities-type data have also been identified as useful, such as: employment data,
congestion data, licensing data, and customs data. Additionally, it is also important to understand the
details of the data requirement itself, which is often referred to as the ‘data quality’. This includes the
reporting frequency, level of aggregation (commercial sensitivity vs. usefulness), standards (eg.
metadata standards), as well as the perspectives of the stakeholders requiring the data.
“Policy leaders are now calling for a renewed focus on productivity growth to ensure Australia
remains internationally competitive in the future.”
24 | P a g e
3. Stakeholder consultation
This section outlines the findings from the stakeholder consultation exercise that comprised two
components, namely:
• interviews with government and industry stakeholders; and
• an online survey distributed to industry stakeholders.
3.1. Interview consultation process
Stakeholder consultation was undertaken with representatives from a range of organisations. The
interview cohort included representatives from government agencies, industry bodies and private
industry.
An initial contact list of approximately 100 individuals working in freight and supply chain related
government agencies and industries was developed and emails were circulated inviting their
participation. Where there was an interest expressed by representatives of other organisations to
participate in the consultation process this was also accommodated by forwarding the same email
invitation. Follow-up phone calls were also undertaken to target organisations where no email
response was received. Representatives from 17 different organisations took part in the consultation
process which took place during November and December 2018:
• Government agencies and regulators: the Australian Bureau of Statistics (ABS), the Bureau of
Infrastructure, Transport and Regional Economics (BITRE); the Department of State Growth
TAS; the Department of Transport and Main Roads QLD; Infrastructure Australia; the National
Transport Commission; the Office of Northern Australia; Roads & Maritime Services NSW;
Transport Canberra & City Services ACT; and Transport for NSW;
• companies/professional services/transport operator: Jacobs; NSW Ports; Pacific National;
RDW Advisory; Telstra; and Virgin Australia; and
• Industry bodies and advocacy groups: Red Meat Advisory Council.
Phone calls were the means used to hold these discussions which tended to run for approximately 60
minutes duration. Stakeholders were typically asked questions covering requirements and
accessibility issues in relation to how data is currently used as well as how it could be better used in
future to inform decision-making in relation to planning, operations and investment areas.
In discussions with stakeholders regarding their data needs and priorities, three key themes were
identified:
• What, where, when and how much? There is a strong demand for a more complete picture
of what goods and finished products are being moved where and when across the transport
network, and the associated value in cost and time and impact terms is needed to provide
opportunities for improved decision-making.
25 | P a g e
• Appropriate level of transparency and aggregation. Data that is provided needs to be suitably
transparent to enable benchmarking whilst also aggregated enough to accommodate
commercial sensitivity.
• Data exchange needs to offer mutually beneficial outcomes. An emphasis on usefulness of
outputs is necessary to encourage improved data sharing between government and firms.
3.1.1. What, where, when and how much?
3.1.1.1. Existing data sources
Several existing data sources were commonly mentioned by stakeholders as being useful for their
planning and investment decision making, and to a lesser extent for operations. A list of these sources
can be found in the WP2 report. While the value in these existing data sources was generally
recognised, it was also acknowledged that improvements to these data sources could be achieved
through better engagement with industry, particularly in relation to data transparency, anticipating
the data needs of industry, and providing access to data on a more regular and timelier basis.
Existing supply of data and data gaps
The transport data that is currently accessible does not enable sufficiently comprehensive insights on
end-to-end supply chain movements to allow monitoring of the associated cost and time
considerations.
An absence of systematic data collection that provides comparative data between different transport
modes and associated infrastructure means there is some rigidity in transport decisions.
With data collation remaining siloed, there is a lack of opportunity to explore the viability of different
options.
Better understanding around corridors of national significance was also a recurring point of interest
in discussions with stakeholders.
“Better focus on investment in the parts of the supply chain that are causing the greatest costs”
"The boundaries that we have via states are not boundaries for states!”
“Would road be more viable than rail?”
“Which particular corridors are carrying the highest value freight?”
26 | P a g e
Some industry stakeholders expressed the need for better transparency around regulatory costs:
The data that are currently available in detailed formats tends to be data that are mandated in
legislation, such as reporting requirements for approval and funding purposes.
Benefits of taking a holistic approach
GPS data, telematics data and Internet of Things (IOT) data are generally viewed as a promising tool
for improving data collection capacity, addressing knowledge gaps as well as enabling opportunities
for efficiency gains:
National productivity and international competitiveness outcomes can only be achieved when there
is end-to-end understanding on time and cost considerations.
While understanding the bottlenecks that exist in the transport network will go some way in
addressing capacity and network capability, having a more holistic understanding of capacity across
the entire network can offer broader advantages.
In summary, objectives for planning and investment should focus on the entire supply chain rather
than individual elements in order to optimise the whole system.
3.1.2. Appropriate transparency and aggregation
Benchmarking
Improved transparency around data formats and granularity was regarded as a key opportunity for
government and industry to undertake benchmarking.
There was some concern that inconsistencies between data collection methodologies amongst
jurisdictions could make benchmarking difficult. However, it is also understood that the higher priority
"Understanding where the costs are in the system; where they accumulate”
“We need to start to access that data and being able to share it could help to optimise
movements and schedules”
"If we keep fixing bottlenecks, we’re basically just pushing the issues to the next bottle neck”
“Gaps in specific data about where there are capacity constraints on the network”
27 | P a g e
is to first establish a baseline of data, as issues with harmonisation could only be addressed once there
is clarity and transparency around the specifics of the data that is available.
Consultations with industry stakeholders indicated that there is an appetite for benchmarking their
performance and competitiveness within their industry both domestically and internationally. The
industry’s willingness to share data seemed to stem from their understanding of how valuable the
outcomes from sharing data would be. In this regard, trust in the quality of data available as well as
the level of aggregation that is required for reporting is also a key factor. This is particularly prevalent
in industries with fewer companies controlling the market share, where the risks to commercial
interests for individual companies are amplified.
Government agencies have already begun sharing data in many cases due to open data policies. Open
data practices can be strengthened through reducing lags between data acquisition and publication.
Commercial issues
In order for industry to share data, there are a number of barriers which would need to be overcome.
These include the manual work involved to classify and categorise the information and provide it in
suitable formats. This could be a significant time investment especially for smaller businesses.
Sharing data is something that most stakeholders expressed as important to improve Australia’s
productivity and competitiveness.
It was also regularly indicated that the return on investment for industry effort in providing data to
government may not be demonstrated or articulated clearly enough.
“There’s a gap between what’s really there and available; secondly what doesn't line up once
there is that transparency”
“Share the data unless you have a really good reason not to”
“Best to start with what's achievable and that helps to build trust to get the harder things
working”
“Being able to do that in a way that benefits everyone, and so no one loses their competitive
advantage”
28 | P a g e
3.1.3. Data exchange needs to offer mutually beneficial outcomes
Mutually beneficial outcomes
Examples of successful data models mentioned by stakeholders typically involved elements of shared
benefits. The data requested by government should help with more focussed investment decisions;
however, it can also be made accessible to industry to improve opportunities for improved
competitiveness on a commercial and operational level.
Costs need to be countered with benefits for industry to better engage in data sharing initiatives. As
noted above, data collection presents an opportunity cost for private firms, as well as potential
competitive and legal risks. In order to encourage the transport sector to participate in any data
sharing initiative, any private benefits that an individual firm might gain would have to outweigh these
costs.
Usefulness of data outputs and data models
With governments becoming increasingly reliant on private sources of data to facilitate their analytical
and policy requirements, a platform for sharing data would allow data sources to be more-easily
combined.
It was generally acknowledged that real-time access to data is not necessary and, in any case, most of
the relevant data is not collected in real time. Interviewees agreed that data should be reported with
roughly the same frequency that it is collected for it to be useful (eg. quarterly collections are reported
quarterly). Another finding from the interviews was that a single data platform could offer a simple
means for storing and providing access to data.
Data models such as Transport for NSW Freight Hub and CSIRO TranSIT were referenced as being
suitable prototypes which could be implemented more widely to facilitate data sharing. The success
of these programs was attributed to delivery being managed by a trusted party to de-identify and
aggregate the data in combination with extensive engagement with industry with reporting provided
at a suitable frequency.
3.2. Online survey
3.2.1. Methodology
The online survey was designed to identify freight data needs for planning, investment and operational
purposes. The analysis aimed to uncover the needs of various industry stakeholders and provide
quantified measures of the value of data sharing and data acquisition from the point of view of these
different stakeholders. The analysis will enable the Australian Government, as this Project’s sponsor,
to have a comprehensive understanding of demand and supply of data and how it can be of value for
the stakeholders.
“We're talking about sucking data out but at what point do we talk about feeding it back in?”
29 | P a g e
In order to answer these main research questions, an online survey was developed, programmed and
fielded among senior management in the freight industry. Government agencies were excluded from
this part of the research process. The survey contained three major components, as follows.
In the first component of the survey, respondents answered questions regarding the entity they were
representing, including:
• the type of entity;
• the entity’s role in the freight supply chain;
• the entity’s industry classification;
• employment size and annual turnover;
• type of cargo handled; and
• which transportation mode is used for the movement of goods.
In the second component, respondents were required to provide information regarding any datasets
that they owned and managed internally. Based on the literature review, the currently available
freight data were classified into 10 main categories and 22 sub-categories. Respondents had the
option to provide other types of category and subcategory if needed. After selecting the relative main
categories and subcategories, respondents were asked about the purpose (planning, investment,
operational) and frequency of use, and if the dataset can be shared.
A similar procedure was used to determine whether firms or industry bodies are using any data
sourced externally. The survey also asked about data acquisition costs. Furthermore, to provide
actionable recommendations to government about which metrics are best suited to improving
national productivity and international competitiveness, several propositions were posed, and
respondents were asked to select all that were relevant or of interest to them and their industry. Note
that these propositions were derived from the findings of the pre-survey focus group (see above).
Respondents were also asked whether they believe there are any gaps in the currently available data
sources.
In the third and last section, respondents were asked to provide answers regarding the current
limitations on data sharing. For this section, respondents were asked to rank the current limitations
for sharing data from most to the least important barrier to sharing.
Data for our analysis came from a sample of 148 senior managers in the freight industry Australian
wide. Respondents were recruited using two sources: 110 respondents were drawn from a panel held
by a major national online panel company, with the remainder being invited via email to participate
in the survey. The survey was administered online from 30th of November until 11th of January 2019,
through a web-based interface. A copy of the survey can be found in Appendix C. Appendix A contains
the detailed results of the online survey.
30 | P a g e
3.2.2. Overview of survey participants
Appendix A provides a description of the survey participants, in terms of activities, size, and other
characteristics.
From the sample of 148 respondents, around 45% were classified as a small business entity (SBE),
around 25% as a medium business entity (MBE) and around 17% as a large business entity (LBE). A
further 7% were from an Industry Association (IA) and the 6% of respondents who selected other were
partly from the local government sector.
Around half of SBEs have less than 20 employees and almost 80% have less than 50 employees. Around
a quarter of MBEs have between 50 to 99 employees, while 20% of LBEs indicated they have more
than 5,000 employees, although many had significantly fewer employees. Most MBEs have higher
than $50 million annual turnover, while LBEs mainly belong to categories with less than $750 million
of annual turnover, with one-third having an annual turnover of between $250 million and $500
million.
Around a third of respondents indicated that they receive commodities, a third said they primarily
acted as a shipper, around 15% of the respondents reported being logistics, transport or carrier type
companies, and a little more than a quarter reported being a service provider to other freight and
logistics companies. Almost 33% of respondents are engaged in national/cross-border operations, and
more than 24% in international operations. A little less than a quarter are active in state and regional
operations.
Most respondent companies handle parcels (32%); large shipments comprising liquid, break and dry
bulk, pallets and containers cover around 41% of the primary cargo of the surveyed businesses.
Respondent SBEs mainly handle parcel and carton, respondent MBEs handle parcels and containers,
and LBEs handle containers, pallets and dry bulk. Respondent SBEs mostly handle consumer and
manufactured goods, MBEs handle manufactured goods, while respondent LBEs handle consumer
goods, manufactured goods and fuel. Transport by road is the dominant mode of transport. SBEs tend
to use road transport, while MBEs and LBEs also rely more on roads, but also rail and water. The
Industry Associations are distributed among all modes.
3.2.3. Summary of findings: Need to measure performance: operation and
planning
Most respondents (67%) noted that they only deal with one category of data. Among these, the
category ‘competitiveness’ is the most commonly internally used data, followed by ‘safety’. For data
that are sourced internally:
• small business enterprises (SBEs) are mainly concerned about competitiveness data;
• medium business enterprises (MBEs) are also concerned with competitiveness and
international gateways performance datasets;
• large business enterprises (LBEs) are interested in market comparisons, but also seem to be
using many different types of data;
31 | P a g e
• labour and infrastructure datasets are the dominating subcategories of the competitiveness
category, which is used commonly by companies;
• operational data is the most commonly indicated purpose of use for internally sourced data,
which is mainly related to competitiveness and performance of international gateways;
• the planning purpose mainly focuses on competitiveness, followed by infrastructure
performance and safety;
• Performance of international gateways, safety, and competitiveness was found to be the most
commonly used types of external data. Among these, safety data appears to be a concern of
SBEs and Industry Associations (IA). For MBEs, competitiveness is the data used the most,
while LBEs are interested in having data on mode-specific transport.
3.2.4. Summary of findings: Data availability
Among the subcategories of data, costs and freight volumes were identified by the respondents as
requiring further supporting data sources. Respondents also said that they require more data for
planning purposes to be made available:
• only 24.7% of the respondents indicated that accessibility to reliable, consistent,
comprehensive and timely data on freight movements is very important; and
• SBEs and MBEs are reasonably satisfied with the available data sources, while LBEs and IAs
considered more data sources to be necessary.
Where identified gaps in the data are concerned, respondents thought that:
• more data should be provided on performance of international gateways, competitiveness,
performance of multimodal networks, Infrastructure performance and regional freight; and
• how data is used by the entities was found to be critical in determining whether a gap is felt
by the respondents; for instance, respondents demanded more data for planning purposes to
be available.
3.2.5. Summary of findings: Data sensitivity and trusted entity
A critical concern of all companies, specifically about the data sourced internally is whether the data
can be shared with others. Almost two-thirds of respondents stated that their data can be shared to
some extent, whereas one-fifth stated that their data can become publicly available. Competition
barriers (34.5%) was seen as the most important critical barrier and challenge for freight data sharing,
followed by resource barriers (29.7%):
• SBEs indicated a reluctance to participate as they are more sensitive to commercial losses as
a result of greater competitive pressures
• MBEs indicated a willingness to share their data, except in cases related to the safety category;
32 | P a g e
• compared to all the other types of companies, industry associations (IAs) seem to be
extremely sensitive to sharing their internally sourced data, regardless of the data type;
• LBEs participating in this survey appear to be concerned about sharing their internally sourced
data. Even when they are happy to share their data, they prefer to make it publicly available
or share it to government agencies instead of other types of agencies.
• Summary of findings: Limitation & barrier to sharing freight data
Overall, concerns about competitors were viewed as the most important critical barrier and challenge
for freight data sharing (34.5%). The cost in terms of necessary resources (29.7%) was viewed as the
second most important barrier. Almost one-third of the sampled participants indicated that they are
currently involved in any existing cooperation between Australian data holders.
Based on the literature review, five categories of barriers were further classified into 20 sub-
categories. Respondents were asked to make choices about these based on a Discrete Choice
Experiment (DCE).2 A DCE asks a respondent to make a choice between a hypothetical set of
alternatives. By altering features of an alternative/good/service in a systematic way in repeated
questions, DCEs use choice frequencies to infer the value associated with product characteristics: how
often a respondent chooses option A over option B indicates how much the respondent values A over
B. DCEs rely on relatively few questions by using principles from the design of statistical experiments
to support inferences about multiple hypothetical ‘what if?’ scenarios. Additionally, ‘best-worst’
scaling asks people not only to report the ‘top’ choice in each choice set, but also the ‘bottom’ choice.
The approach adopted elicited the following findings:
• Overall, ‘disclosure of individual shipment or company data’ is viewed as proprietary or
business-sensitive, while ‘data sharing with foreign countries’ was ranked the least (or equally
least) important factor.
• For SBEs, disclosure of individual shipment or company data is viewed as proprietary or
business-sensitive ranked 1, but the same concern was ranked 2 for LBEs and IAs, and ranked
3 for MBEs.
3.3. Focus groups
Several focus groups were held as part of the consultation process. The participants of these focus
group were largely executive-level personnel and/or principal industry consultants.
2 Discrete Choice Experiments (DCEs) are a type of Stated Preference elicitation approach embedded in random utility theory (Thurstone 1927). DCE methodology makes use of choices rooted in real life that provide testable predictions (Louviere et al. 2000). DCEs, an alternative to the revealed preference method, systematically vary combinations of levels of each attribute, to reveal new opportunities relative to the existing circumstance of attribute levels on offer.
33 | P a g e
3.3.1. Pre-survey focus group
The first focus group was held before the online survey was distributed. The purpose of this focus
group was to get an initial understanding of the views of the industry stakeholders in terms of freight
data needs. The focus group discussed the following questions:
• What data is needed to improve national productivity and international competitiveness?
• What data does industry need to enhance their businesses?
• What does the industry want from government to better run their businesses?
In discussions it became apparent that the main priority for businesses was to satisfy their customers’
needs. It was also noted that taking a national approach may pose a risk that state jurisdictions might
not be fully engaged, especially since state jurisdictions are competing against each other.
The discussion was then directed to establishing the understanding around freight performance
indicators. The following points were made:
• Three key metrics are: unitised cost, size of supply chain, service (related to time), and
reliability (consistency). Note that cost only related to freight transport, not the cost of goods
themselves.
• Forecast and projection data are also needed for planning and investing. This is also important
to ensure that the industry can analyse the data to come up with better ways to run their
business, if necessary.
• Performance indicators and comparisons can be done separately for each of the supply chain
components, as well as for each mode.
• Current data is fragmented, eg. inconsistent update frequency. However, various cost data is
already available (eg. stevedore reports, waterline reports)
The discussion also included identification of characteristics of data that would be required. The main
comments that were received indicated that:
• Data should be anonymous, which might represent a problem if participation is low so that
entities could be identified;
• There would need to be trust in the accuracy of the data and data custodians;
• Data collection should be light touch, low cost or funded, harmonised, and low frequency or
automated;
• Data should be internationally benchmark-able (if aggregation uses percentage, the data
might not be useful for international benchmarking); and
• Governance does not really matter as long as the data is anonymised; for instance, if a trusted
independent body holds the data.
34 | P a g e
Finally, the discussion focused on identifying several pressing issues that could be resolved with the
help of data:
• Bulk commodities. Australia’s significant supply chains carry bulk commodities, particularly
iron ore, coal and LNG. While they are already among the world’s most productive it is in our
national interest to protect and enhance these supply chains. Learning about their best
practise productivity metrics, capital allocations, service standards and regulatory
environments may provide a framework to improve national productivity.
• Non-express domestic forwarding (FTL, LTL, Rail, Sea). This is another significant logistics
component in Australia, encompassing various modes of transport including road, rail, and
sea, as well as both FTL and LTL. The efficiency of our linehaul journeys is a direct contributor
to national productivity and, hence, framing the most fit for purpose metrics is vital.
• Import/export containers and national gateways. Australia is a significant importer of
containerised goods and our container ports are our national gateways. The more cheaply and
reliably we can import and export goods the more productive our economy will be. We need
to consider the most effective metrics to drive national productivity improvements
considering the stevedoring component as well as transport within the port and road and rail
land-side transport outside the port to the consignee.
• Agricultural goods. Agricultural exports have been important to Australia for more than two
centuries. Competing on a global basis means our farm goods must get to market reliably
while retaining their high quality.
• Express, e-commerce and first and last mile deliveries. This is the fastest growing part of the
logistics sector especially as a result e-commerce sale. The big challenges are time and
reliability of delivery as well as cost. The national productivity challenge here is to find metrics
that can lead to increased efficiency in congested areas, tight timeframes, problems such as
access to loading zones and against a backdrop of too many failed deliveries.
• Land planning and corridor protection. Efficient supply chains require seamless networks and
sites where goods can be consolidated and separated out cheaply, reliably and quickly. A real
focus on supply chain needs by planners and policy makers across governments is necessary
to improve productivity. Access to appropriately zoned land at key transport nexus points is
vital. Similarly, freight corridors of all modes and their entry and exit points should be
protected from encroachment to ensure that safe high productivity transport can easily be
used.
3.3.2. Post-survey focus group
The final focus groups were held following the distribution of the online survey and towards the end
of the interview consultation process. These focus groups aimed to confirm the findings of the other
stakeholder consultation activities.
35 | P a g e
A list of identified data gaps and priorities were provided as a starting point for discussion. There was
consensus amongst participants that the list covered most of the data gaps and priorities. The
following additional key points were made:
• Planning of network extensions, freeways and other infrastructure investments in the pipeline
are not transparent, restricting opportunity for industry to make optimal decisions;
• The data that is currently accessible is mostly operational; relatively little is readily accessible
from a planning point of view; and
• There was perceived to be a lack of communication and sharing of information between
government departments and agencies.
A list of principles of open freight data were provided as a starting point for discussion. There was
general agreement that these principles were not currently being implemented, but agreed that
implementation would be difficult, for instance in relation to road freight data. Respondents also
noted that it can be difficult to properly de-identify data and to ensure that the data are not
commercially sensitive. Thus, a good understanding of the market often means that data sources can
be identified.
Regarding sharing industry operational data, the following points were made:
• There are considerable issues around the competitive advantage aspects for industry in
protecting their data;
• Existing confidentiality agreements with key customers are a concern; customers may not
wish data to be disseminated; and
• Government should also be sharing operational data to encourage measurement of its
performance.
3.4. Summary of findings
Table 3-1 summarises the findings of the stakeholder consultation.
36 | P a g e
Table 3-1. Summary of online survey findings and focus groups
Industry Industry Association
Small Medium Large
DA
TA IN
USE
Sou
rced
inte
rna
lly
Data category 1-Competitiveness 1-Competitiveness 1-Safety 2-Regional freight
1-Competitiveness 2-Performance of multimodal networks 3-Safety 4- Regional freight 5-Mode-specific transport data
Data sub-category 1-Labour, 2-E-commerce, 3-Value of freight
1-Roads tracks bridges tunnels
1- Labour 2- Freight volumes
1 & 2 & 4 - Landside logistics costs 3 & 5 - Freight volumes
Data purpose Operation and Planning 1-Investment 1-Planning operation & investment
1-Planning and investment 2-Planning and operation 3-Planning and operation 4-Planning operation and investment 5-Planning operation and investment
Frequency of use Every day Once a week 1- Once a week 2-Everyday Once a month
Data sharing Yes, publicly to anyone Yes, publicly to anyone No, the data cannot be shared with anyone at all
No, the data cannot be shared with anyone at all
Sou
rced
exte
rna
lly
Data category 1- Competitiveness 2-Safety
Competitiveness 1- Regional freight 2-Performance of international gateways 3-Mode-specific transport data
1-Performance of international gateways 2- Performance of multimodal networks 3- Safety
37 | P a g e
Data sub-category 1-E-commerce & Congestion metrics 2-Volumes & Airports
Labour 1- Landside logistics costs 2- Rail 3-Road
1- Ports 2- Landside logistics costs 3- Road
Data purpose Operation Operation Planning 1- Planning operation & investment 2- Planning & investment 3- Operation & planning
Frequency of use 1-Every day 2-Two to three times a week
Once a month 1- Every day 2- Every three months 3- Every day
1 & 2-Every year or more 3- Every three months
Cost to access data Less than $1000 Less than $1000 Less than $1000 Less than $1000
GA
PS
IN
DA
TA
Generally, No Generally, No Generally, Yes Generally, Yes
MO
ST
IMP
OR
TAN
T B
AR
RIE
RS
FOR
DA
TA
SH
AR
ING
1-Disclosure of individual shipment or company data is viewed as proprietary or business-sensitive 2-Lack of financial subsidies for data sharing make it difficult to keep all partners interested in and committed to participation 3- Data source diversity and in some cases the large amounts of data requires costly processing
1-Sensitivity about sharing information which could be used by competitors 2-Compatibility issues between national freight data sets 3-Sharing across international boundaries is difficult as is coordination with multiple international agencies
1-Sensitivity about sharing information which could be used by competitors 2-Disclosure of individual shipment or company data is viewed as proprietary or business-sensitive 3-Data source diversity and in some cases the large amounts of data requires costly processing
1-Sensitivity about sharing information which could be used by competitors 2-Disclosure of individual shipment or company data is viewed as proprietary or business-sensitive 3-Compatibility issues between national freight data sets processing & Private sector interests do not always align with the public good
*note that the numbers in each cell in column correspond with each other.
38 | P a g e
4. Conclusions
The freight supply chain industry, both in Australia and overseas, recognises that access to better
freight data can improve firm and industry performance as well as enabling investment in the network
to be better targeted.
4.1. Key findings
4.1.1. Main themes
In discussions with stakeholders regarding their data needs and priorities, three key themes were
identified:
• What, where, when and how much? There is strong demand for a more complete picture of
what goods (bulk, non-bulk, containers) are being moved where and when across the
transport network because of the potential savings in cost and time from improved decision-
making.
• Appropriate transparency and aggregation. A key trade-off is that the provision of data needs
to be suitably transparent to enable benchmarking whilst also aggregated enough to
accommodate commercial sensitivity.
• Data exchange needs to offer mutually beneficial outcomes. An emphasis on the potential
usefulness of outputs is necessary to encourage improved data sharing.
The key points from this research project are illustrated in the figure below.
Figure 4-1. An illustration of the key findings
39 | P a g e
4.1.1.1. Performance metrics: movements, cost, time, and capacity
The fundamental need expressed by most stakeholders is to learn about the performance and
competitiveness of some aspect of the national supply chain. The metrics sought depend on the
stakeholders’ interests and the scope of the decisions they are seeking to support. However, the
underlying data that serve this purpose relate to four aspects:
• Goods movements (“what, where, when, and how much”);
• Associated costs;
• Time (i.e. service level and reliability); and
• Capacity (i.e. utilisation, congestion, and infrastructure conditions).
The consultation process revealed that stakeholders prioritise data on cost and volume (freight task)
ahead of the other aspects. However, some other contextual datasets, such as infrastructure condition
data and employment data are also frequently sought.
Our review of previous reports revealed the importance of economic competitiveness (productivity,
efficiency, and reliability). This study, particularly from online survey, reinforced this view. We found
that business entities, particularly small business entities, commonly seek insights into the
competitiveness of their operation, whereas governments, larger firms and industry associations are
more concerned about planning and investment decision-making.
In addition to this attention to economic competitiveness, the study also identified the importance of
end-to-end network visibility, which enables decision makers to identify problems (eg. bottlenecks)
and reduce waste of time and effort, in supply chains.
The study also identified the importance of: nationally significant freight corridors; first/last-mile
deliveries; urban freight; gateways; capacity management; and data requirements for modelling
purposes.
4.1.1.2. Interdependent relationships
It has been observed that industry, state, federal, and local government stakeholders are partners in,
an interdependent relationship, in the sense that there is an inter dependence (and shared
responsibility) between government and industry to fulfil freight data needs. Governments have an
obligation to manage the transport networks, which are used by the freight industry but only the
freight industry can report the use they actually make of those networks. Freight data typically has
both ‘private’ and ‘public good’ value. The challenge is to find ways by which the government can
invest in collecting and collating privately held data to generate public value without destroying the
private value of that data in the process.
To do this, greater trust needs to be created between the government and the industry. To facilitate
this, there may be a need for a neutral entity that can take responsibility for undertaking data pre-
40 | P a g e
processing steps and data aggregation (to ensure commercial confidentiality) before distributing it for
other stakeholders to use.
4.1.1.3. Transparency on benefits
The industry has shared their concerns on data sharing in several fora including in submissions to
major recent public inquiries. In general, they are not opposed to sharing their operational data to
help improve the efficiency and productivity of supply chains.
Despite being willing to share their data, the industry was reluctant to make commitments and/or
undertake new initiatives. This is mainly due to industry uncertainty around the benefits they would
derive in return for the effort they must make to share their data. Industry expressed scepticism about
the value they have received to date from their data sharing in the past. Some of the concerns
expressed were:
• Lack of timeliness on datasets delivery/dissemination;
• Lack of systematic data collection;
• Lack of end-to-end visibility due to fragmented datasets; and
• Lack of traction from previous initiatives on establishing some sort of ‘data centre’.
Participants also indicated:
• They would be unwilling to share commercially sensitive data; and
• They sought that the effort and cost to them of additional data collection and processing (for
sharing purposes) should be either minimal or funded by government. Alternatively, they
welcomed the prospect of low-cost automated processes. This view was strong among
smaller business entities, but less of an issue for larger businesses.
4.1.1.4. Learning from existing datasets
The study also identified several existing programs and associated datasets and tools that are
considered to be particularly useful. These include: BITRE yearbook, ABS surveys (Motor Vehicle Use
and Freight Movement), CSIRO’s TraNSIT and TfNSW Freight Performance Dashboard.
However, it was frequently commented that the available data is lacking in one respect or another.
Common observations were that:
• Data updates are too infrequent;
• Timeliness of delivery is often lacking; and
• The level of aggregation and presentation of the datasets is not suitable for the needs of the
users.
4.1.1.5. Datasets in greatest demand
The study has clearly identified several datasets that are in demand:
41 | P a g e
• Most notably, freight movement data (at various granularity levels); and,
• more broadly, performance indicators of the supply chains; particularly cost and time
components of goods movement. Costs, service levels, and reliability are the most typically
used measures of performance.
Segments of supply chains that were identified as needing greater clarity are:
• urban freight;
• first/last mile;
• regional issues;
• gateways;
• nationally significant corridors; and
• issues related to some specific commodities.
Respondents commented that the eventual goal is to achieve holistic freight data coverage in order
to provide end-to-end visibility for the decision makers.
4.1.1.6. Better coordination is required
The literature review and stakeholder responses suggest that the deficiencies associated with
currently available datasets stem more from collection procedures and information
delivery/dissemination rather than the subject matter being collected. It appears that there are more
issues associated with the ‘how it is being collected and disseminated’ than with the ‘what is being
collected’.
Table 4-1 below summarises the main findings of this study.
42 | P a g e
Table 4-1. Summary of findings of this study
Industry Industry Association
Government Small Medium Large
Owned data
• Competitiveness, performance of gateways, and regional freight are the top
three datasets sourced internally
• Specifically, the popular subcategories are labour and infrastructure
competitiveness, as well as regional freight volumes.
• The data is used mainly for operation purpose, and the data used for this
purpose is mainly on competitiveness, safety, and performance of gateways
• Competitiveness data is used commonly for all three purposes
• Frequency of data use is high, at least weekly
• Generally using
many types of
data
• The most
popular
subcategory is
landside
logistics costs
• Generally using
their data for all
three purposes
• Frequency of
data use is
month, less
compared to
business entities
• Various government datasets
including:
• IAP telematics data
• ABS surveys
• BITRE statistics
• (A full listing of identified data
sets is reported in WP2 report)
• SBEs mainly collecting
competitiveness data,
used for both planning
and operation
• MBEs are mainly
collecting
infrastructure
competitiveness
data, which is used
for investment
purpose
• The data can
generally be shared
publicly
• Quite engaged in
volume and labour
subcategories
• Using all types of
data, with focus on
safety, regional
freight, and
performance of
gateways
• LBEs typically use
their data for all three
purposes
Data needs • Better transparency around regulatory cost
• To benchmark performance and competitiveness both locally and globally
• Better understanding around
corridors of national significance
43 | P a g e
Industry Industry Association
Government Small Medium Large
• To understanding performance of gateways and safety issues
• More data is requested for planning and operation
• More data on competitiveness, performance of multimodal networks,
regional freight, and infrastructure performance is requested
• Generally, they do not pay more than $1,000 to access external data
• Frequency of use of external data is generally lower, compared to internally
sourced data.
• Planning for infrastructure investment and general freight processes are
identified as key needs
• Understanding operational reliability of public transport infrastructure
• Requesting
more data
sources
• Ports data is the
most commonly
sought-after
subcategory,
followed by
landside
logistics cost
• Generally, they
do not pay more
than $1000 to
access data
• GPS, telematics data and IoT is a
promising tool to collect more
data that will enable
opportunities and efficiency
gains
• Holistic understanding of
capacity across the entire
network
• Freight transport regulators
require freight operator
performance (speed, fatigue,
load restraint, mass, vehicle
maintenance etc) and Data to
improve regulator safety
confidence to allow higher
productivity vehicles
• Generally happy with
availability of data
• Generally happy
with availability of
data
• Requesting more data
sources
• Landside logistics cost
is the most commonly
sought-after
subcategory
Barriers/ likeliness to share data
• In general, data sensitivity and commercial confidentiality is the main barrier
for sharing
• Another barrier of data sharing is the lack of standardisation of diverse
datasets
• It is quite likely that competitiveness data can be shared publicly, yet there is
also a large group of respondents stating that it cannot be shared
• Safety-related data is another type of data that generally cannot be shared
• Very sensitive to
share data,
since likely they
are in no
position to
share data from
their members
• Government representatives are
generally more open to share
data, yet there might be some
governance and institutional
barriers across borders
44 | P a g e
Industry Industry Association
Government Small Medium Large
• Stakeholders are more likely to share data to government agencies or
departments
• Data sharing can be more readily done with appropriate data governance:
simplification, harmonization, cost-benefit, outcomes focused, fit for
purpose, seat at table
• More sensitive to
competitiveness data
• More open to
share, yet more
concerned with
sharing safety data
• Concerned about data
sharing, likely due to
market domination
prevents sufficient
anonymisation
• More likely to share
with government
rather than other
entities
45 | P a g e
References
ABS 2011, A future Australian transport satellite account: ABS views.
ABS 2005, Freight movement survey – Rail, sea and air freight, the year ending 31 March 2001.
ABS 2015, Road freight movements, 12 months ended 31 October 2014.
ABS 2017, Survey of motor vehicle use, 12 months ended 30 June 2016.
ACCC 2018, Container stevedoring monitoring report 2017-18.
ACS 2017, Data sharing frameworks – Technical white paper.
ALC & ACIL Allen Consulting 2014, The economic significance of the Australian logistics industry.
ALC 2018, A common data set for our supply chain: Developing and implementing the national
freight and supply chain strategy – Discussion paper 2.
ARRB 2016, Freight movement data collection service, concept proposal.
Australian Railway Association & IISRI 2018, Smart rail route map – Phase 1 interim report.
Austroads 2006, Review of freight data collection and generation procedures, AP-R283-06.
Austroads 2011, Impact of light commercial vehicles/rigid trucks in urban networks – Scoping study,
AP-R376/11.
Austroads 2014, Light freight vehicles and urban logistics, AP-R457-14.
Austroads 2015, Concept of operations for C-ITS core functions, AP-R479-15.
Austroads 2016, Overcoming barriers to the off-peak movement of freight in urban areas, AP-R520-
16.
Austroads 2018a, Establishing extended hours delivery trials, AP-R586-18.
Austroads 2018b, Austroads data standard – Knowledge sharing framework, AP-T338-18.
Austroads 2018c, Modelling for high productivity vehicles in metropolitan areas, AP-R558-18.
Baumgartner H. & Steenkamp J.-B. E. 2001, “Response styles in marketing research: A cross-national
investigation”, Journal of Marketing Research, 38(2), 143-156.
BITRE 2018a, Australian infrastructure statistics – Yearbook 2018.
BITRE 2018b, Waterline 62 – October 2018.
BITRE 2018c, Trainline 6.
BITRE 2018d, Freightline 7 – Australian aluminium industry freight transport.
46 | P a g e
BITRE 2018e, Forecasting Australian transport: A review of past bureau forecasts, Research report
149.
Budd L. & Newstead S. 2014, Potential safety benefits of emerging crash avoidance technologies in
Australian heavy vehicles, MUARC report no. 324.
Camargo P. & Walker L. 2017, “Freight modelling in Australia: what we've got, what do we need and
how are we getting there?”, in Proceedings of the AITPM National Traffic and Transport Conference.
Chapman R. G. & Staelin R. 1982, “Exploiting rank ordered choice set data within the stochastic
utility model”, Journal of Marketing Research, 288-301.
CISCO 2018, Towards a multimodal transportation data framework: Supporting emerging
transportation models and services.
CSIRO 2018, Transport Network Strategic Investment Tool (TranSIT) [online], URL:
https://www.csiro.au/en/Research/LWF/Areas/Landscape-management/Livestock-
logistics/TRANSIT, accessed: 18 December 2018.
DG Cities 2018, Modelling future mobility, D3.10, GATEway project.
DIRDC 2018a, Inquiry into national freight and supply chain priorities.
DIRDC 2018b, National infrastructure data collection and dissemination plan.
Finn A. & Louviere J. J. 1992, “Determining the appropriate response to evidence of public concern:
the case of food safety”, Journal of Public Policy & Marketing, 12-25.
Flynn T. N. 2010, “Valuing citizen and patient preferences in health: recent developments in three
types of best–worst scaling”, Expert review of pharmaco-economics & outcomes research, 10(3),
259-267.
Heaney 2013, Supply chain visibility: A critical strategy to optimize cost and service, Aberdeen
Group.
Hensher D., Rose J. M. & Greene W. H. 2005, Applied choice analysis: a primer, Cambridge University
Press.
Hensher D., Teye C. & Ellison R. 2018, Commodity interaction in freight movement models for
Greater Sydney – Working paper, ITLS-WP-18-05.
Helson H. 1964, Adaptation-level theory: an experimental and systematic approach to behavior.
Holguín-Veras J., Wang C., Browne M., Hodge S. D. & Wojtowicz J. 2014, “The New York City off-hour
delivery project: Lessons for city logistics”, Procedia – Social and Behavioral Sciences, 125, 36–48.
IPA 2018, Fixing freight: Establishing freight performance Australia.
ISO 2008, Intelligent transport systems — Integrated transport information, management and
control — Data quality in ITS systems, Technical report ISO/TR 21707, 1st ed.
47 | P a g e
ISO 2010, Intelligent transport systems — Freight land conveyance content identification and
communication — Part 1: Context, architecture and referenced standards, ISO/TS/ 26683-1.
ITF 2015, Big data and transport: Understanding and assessing options.
Katsikides N. n.d., Freight performance measure program [presentation].
Keever & Pol 2002, “Data fusion to support information needs for surface transportation operators”,
in Proceedings of the 9th ITS World Congress.
KPMG & Arup 2017, Travel demand & movement patterns report, Infrastructure Victoria.
KPMG 2018, Supply chain indicators scoping report.
Lee J. A., Soutar G. N. & Louviere J. 2007, “Measuring values using best‐worst scaling: The LOV
example”, Psychology & Marketing, 24(12), 1043-1058.
Louviere J. J., Hensher D. A. & Swait J. D. 2000, "Stated choice methods: analysis and applications,
Cambridge University Press.
Marley A. A. & Louviere J. J. 2005, “Some probabilistic models of best, worst, and best–worst
choices”, Journal of Mathematical Psychology, 49(6), 464-480.
Meuleners L., Hendrie D., Legge M. & Cercarelli L. R. 2002, An evaluation of the effectiveness of the
black spot programs in Western Australia, RR 155.
NTC 2016a, Who moves what where.
NTC 2016b, National land transport productivity framework – Issues paper.
NTC 2017, Supporting good decision to improve transport productivity.
Productivity Commission 2017, Data availability and use – Inquiry report.
SBEnrc 2017, Mining the data-sphere: Big data, technologies, and transportation – Congestion
management.
Taniguchi & Thompson (eds.) 2015, City logistics: Mapping the future, CRC Press.
TCA 2018, Intelligent access program (IAP) [online], URL: https://tca.gov.au/truck/heavy-vehicle-
applications/iap, accessed: 18 December 2018.
TfNSW 2018, NSW freight and port plan.
TfV 2018, Delivering the goods: Victoria freight plan.
Thurstone L. L. 1927, “A law of comparative judgment”, Psychological review, 34(4), 273.
TIC 2016, National policy framework for land transport technology.
TMR 2013, Moving freight: A strategy for more efficient freight movement.
Tziotis M. 1993, Evaluation of mid-block accident ‘Black spot’ treatments, MUARC report no. 48.
48 | P a g e
Ueda S. 2017, C-ITS: Joint TC278/WG16-TC204/WG18 meeting, France, April 2017.
49 | P a g e
Appendix A. Detailed online survey results
A.1. Overview of survey respondents
A.1.1. General overview and activity
From the total sample of 148 respondents, around 45% were classified as a small business entity (SBE),
around 25% as a medium business entity (MBE) and around 17% as a large business entity (LBE). A
further 7% were from an Industry Association (IA) and the 6% of respondents who selected other were
from the local government (Figure 4-2).
Figure 4-2. What sort of entity are you responding on behalf of?
In terms of the primary role of the entity, around 30% indicated they are receiving commodities,
around 29% indicated their primary role as a shipper, and around 15% of the respondents reported
being logistics, transport or carrier type companies. Around 26% of the entities reported being a
service provider to other freight and logistics companies or individuals (Figure 4-3).
45.3%
25.0%
16.9%
6.8%
6.1%
Small business entity: Less than $10 millionturnover
Medium business entity: Between $10m and$250 million turnover
Large business entity: Greater than $250 millionturnover
Industry Association responding on an industrybasis
Other
50 | P a g e
Figure 4-3. Entity role in the freight chain?
We also asked respondents to identify their industry classification using the ANZSIC 4-digit level
classification system.3 Over one-quarter of companies identified as part of the transport services
industry, with the remaining companies spread across several services industries as well as a small
percentage of firms operating in the mining and manufacturing sectors. Most firms (around 26%) are
in the transport sector (Figure 4-4) with the remaining firms covering a broad range of sectors,
including accommodation and food services (6.6%), manufacturing (5.6%), and agriculture (4.8%).
3 Australia and New Zealand Standard Industry Classification system.
51 | P a g e
Figure 4-4. Please select which industry classification(s) best applies to your entity?
6.6%
4.7%
4.8%
4.4%
4.0%
4.1%
4.4%
3.4%
2.5%
2.6%
5.6%
2.6%
5.2%
3.4%
2.4%
4.1%
5.2%
3.0%
3.3%
2.8%
3.9%
3.8%
4.2%
2.1%
4.1%
2.9%
Accommodation and food services
Administrative and support, wastemanagement
Agriculture, forestry, fishing and hunting
Arts, entertainment and recreation
Construction
Educational services
Finance and insurance
Health care and social assistance
Information and cultural industries
Management of companies and enterprises
Manufacturing
Mining, quarrying and oil and gas extraction
Professional, scientific and technical services
Public administration
Real estate and rental and leasing
Retail trade
Transport - Road transport
Transport - Postal and Courier pick-up anddelivery services
Transport - Maritime transport
Transport - Aviation transport
Transport - Rail transport
Transport-Transport support services
Transport - Logistics-warehousing and storageservices
Utilities
Wholesale trade
Other services (except public administration)
52 | P a g e
A further split down of industry categories is presented in Figure 4-5 where the percentage of shippers,
carriers, service providers and receivers are shown for each industry categories. Firms represent a
wide cross-section of industrial classification, confirming the breadth of the supply-chain industry and
the robustness of the survey.
Figure 4-5. Please select which industry classification(s) best applies to your entity?
53 | P a g e
Figure 4-6 asks about the role of the respondent within the supply chain. Almost 33% of firms are
engaged in national/cross-border operations, and more than 24% in international operations. A little
more than a quarter engaged in state and regional operations.
Figure 4-6. At which level is your entity involved?
A.1.2. Respondents by employment size
Table 4-2 shows the breakdown of employment size based on the entity type representation. The
sample includes a diverse set of companies with various amounts of annual turnover.
Around half of SBEs have less than 20 employees and almost 80% have less than 50 employees (Table
4-2). Around a quarter of MBEs have between 50 to 99 employees. In the sample, 20% of LBE indicated
they have more than 5000 employees, although many had significantly fewer employees.
8.4%
4.6%
9.2%
12.9%
6.2%
5.9%
13.5%
4.9%
4.3%
7.3%
3.8%
6.5%
3.8%
4.0%
4.9%
Agricultural Commodities
Coal
Construction Materials
Consumer Goods
Forestry
Fuel
Manufactured goods
Metro Containers
Minerals
Automotive
Oil Seeds
Steel
Waste
Other
I don't know
54 | P a g e
Table 4-2. Participants’ employment size based on the entity type representing
Around half of industry associations have more than 5,000 employees (Table 4-3), while smaller
companies are evenly distributed into two categories of less than 500 and more than 2,500 employees.
Table 4-3. Participants’ employment size based on the Industry Association representing
Employee size is broadly aligned with the revenue/expenditure of a company as seen in Figure 4-7,
Figure 4-8, Figure 4-9, and Figure 4-10:
• The annual turnover of MBEs is significantly larger than that of SBEs as the majority of MBEs have
higher than $50 million annual turnover. Having said that the turnover of the MBEs does not
frequently exceed $200 million (limited to 10.6%).
• LBEs mainly belong to categories with less than $750 million of annual turnover, where one-third
have an annual turnover of between $250 million and $500 million.
Small
Business
Medium
Business
Large
Business Other
Less than 20 employees 52% 3% 0% 63%
20 to 49 employees 26% 5% 0% 0%
50 to 99 employees 12% 24% 8% 0%
100 to 199 employees 6% 16% 8% 0%
200 to 349 employees 2% 22% 12% 13%
350 to 499 employees 0% 8% 4% 0%
500 to 999 employees 2% 16% 16% 25%
1000 to 2499 employees 0% 3% 16% 0%
2500 to 4999 employees 2% 0% 16% 0%
5000 plus employees 0% 3% 20% 0%
Total count 34 19 10 2
Industry
Association
Less than 500 employees 20%
2500 to 4999 employees 20%
5000 or more employees 50%
I don't know 10%
55 | P a g e
Figure 4-7. Small business entities annual turnover before tax
Figure 4-8. Medium business entities annual turnover before tax
11.9%
26.9%
32.8%
17.9%
6.0%
4.5%
Zero to less than $50,000
$50,000 to less than $200,000
$200,000 to less than $2 million
$2 million to less than $5 million
$5 million to less than $10 million
I don't know
21.6%
29.7%
32.4%
8.1%
5.4%
2.7%
$10 million to less than $50 million
$50 million to less than $100 million
$100 million to less than $150 million
$150 million to less than $200 million
$200 million to less than $250 million
I don't know
56 | P a g e
Figure 4-9. Large business entities annual turnover before tax
Figure 4-10. Industry association annual turnover before tax
A.1.3. Entities and their activities
The following graphs provide an indication of the types of entities participating in the survey based on
the commodity they deal with, noting that service providers are excluded. Most companies deal with
parcels (32%) while large shipments comprising liquid, break and dry bulk, pallets and containers cover
around 41% of the primary cargo of the surveyed businesses (Figure 4-11). Most of the businesses
38.2%
8.8%
14.7%
8.8%
11.8%
17.6%
$250 million to less than $500 million
$500 million to less than $750 million
$750 million to less than $1 billion
$1 billion to less than $3 billion
$3 billion or more
I don't know
10.0%
10.0%
30.0%
10.0%
20.0%
20.0%
Zero to less than $500 million
$2 billion to less than $5 billion
$10 billion to less than $50 billion
$50 billion to less than $100 billion
$100 billion or more
I don't know
57 | P a g e
surveyed (80%) also deal with a second type of cargo, where carton again dominates (27%), followed
by various bulk goods and pallets (Figure 4-11Figure 4-12).
Figure 4-11. The primary type of cargo entities are involved with
Figure 4-12. The second main type of cargo entities are involved with
To further analysis the type of cargo the respondents are involved with, Table 4-4 provides a cross
tabulation of the primary and secondary cargo types. A closer look at the tables reveals that parcel
32%
8%
12%
20%
7%
1.4%
1%
10%
7%
Parcel
Carton
Pallet
Container
Dry bulk
Break bulk
Liquid bulk
Other
I don't know
8%
27%
13%
8%
11%
8%
2%
5%
18%
Parcel
Carton
Pallet
Container
Dry bulk
Break bulk
Liquid bulk
Other
Not involved with any other type of cargo
58 | P a g e
and carton cargo types are correlated with each other, while other types are typically fall into the large
cargo categories such as bulk, pallet and container.
Table 4-4. What is the primary type of cargo your entity is involved with? * What is the second main type of cargo your entity is involved with? Cross-tabulation
The cargo types that are mainly dealt with by SBEs are parcel and carton, for MBEs they are parcels
and containers, and LBEs handle pallets and dry bulk. Further, the respondents falling into the industry
association category are mainly involved in larger other cargo types.
Table 4-5. What sort of entity are you responding on behalf of? * What is the primary type of cargo your entity is involved with? Cross-tabulation
Figure 4-13 suggests that there is a fairly even distribution of the commodities handled by the survey
respondents. Manufactured goods and consumer goods comprise 13.5% and 12.9%, respectively, but
construction materials and agricultural commodities are also important.
Parcel Carton Pallet Container Dry bulkBreak
bulk
Liquid
bulkOther
Not involved
with any other
type of cargo
Total
Parcel 0 24 7 0 1 3 0 0 11 46
Carton 4 0 3 2 1 1 0 0 0 11
Pallet3 8 0 5 1 1 0 0 0 18
Container 2 3 5 0 8 4 3 1 3 29
Dry bulk 0 0 2 4 0 2 0 0 1 9
Break bulk 0 1 0 0 1 0 0 0 0 2
Liquid bulk 0 0 0 0 0 0 0 0 1 1
Other 1 0 0 0 2 0 0 5 7 15
Total 10 36 17 11 14 11 3 6 23 131
What is the second main type of cargo your entity is involved with?
Wh
at
is t
he
pri
ma
ry t
yp
e o
f c
arg
o
yo
ur
en
tity
is
in
vo
lve
d w
ith
?
Parcel Carton Pallet Container Dry bulkBreak
bulk
Liquid
bulkOther I don't know Total
Small
business 33 7 5 5 2 1 1 6 7 67
Medium
business 11 4 5 11 3 1 0 0 1 36
Large
business 2 1 6 9 5 0 0 1 1 25
Industry
Association 0 0 1 3 1 0 0 4 1 10
Other 1 0 1 2 0 0 0 4 1 9
Total 47 12 18 30 11 2 1 15 11 147
What is the primary main type of cargo your entity is involved with?
Wh
at
so
rt o
f e
nti
ty a
re y
ou
res
po
nd
ing
on
be
ha
lf o
f?
59 | P a g e
Figure 4-13. Please specify which commodity groups you work with
Table 4-6 through Table 4-9 show a breakdown of commodities based on the entities and their role in
the freight chain. For entities that ship goods, SBEs mostly handle consumer and manufactured goods,
MBEs manufactured goods, LBEs consumer goods, manufactured goods and fuel, while members of
an industry association handle a range of goods (Table 4-6). This pattern is very similar for entities that
receive goods (Table 4-7), for entities that provide goods (Table 4-8), and for those that are carriers of
goods (Table 4-9).
8.4%
4.6%
9.2%
12.9%
6.2%
5.9%
13.5%
4.9%
4.3%
7.3%
3.8%
6.5%
3.8%
4.0%
4.9%
Agricultural Commodities
Coal
Construction Materials
Consumer Goods
Forestry
Fuel
Manufactured goods
Metro Containers
Minerals
Automotive
Oil Seeds
Steel
Waste
Other
I don't know
60 | P a g e
Table 4-6. Cross-tabulation between entity types and commodity groups, if the entity is a shipper of goods
What sort of entity are you responding on behalf of?
Counts Small
business Medium business
Large business
Industry Association
Other
Ple
ase
sp
ecif
y w
hic
h c
om
mo
dit
y gr
ou
ps
you
wo
rk w
ith
?
Agricultural Commodities 4 2 5 3 2
Coal 4 1 3 1 1
Construction Materials 6 4 5 1 2
Consumer Goods 15 2 9 3 1
Forestry 4 3 4 1 1
Fuel 4 3 6 0 2
Manufactured goods 8 10 8 3 1
Metro Containers 1 0 4 2 0
Minerals 2 0 3 2 1
Automotive 6 3 5 2 1
Oil Seeds 1 1 3 1 0
Steel 2 4 4 4 2
Waste 0 1 3 1 3
Other 5 0 0 1 0
I don't know 4 2 0 0 1
61 | P a g e
Table 4-7. Cross-tabulation between entity types and commodity groups, if the entity is a receiver of goods
What sort of entity are you responding on behalf of?
Counts Small
business Medium business
Large business
Industry Association
Other
Ple
ase
sp
ecif
y w
hic
h c
om
mo
dit
y gr
ou
ps
you
wo
rk w
ith
?
Agricultural Commodities
4 2 3 2 2
Coal 3 1 2 0 1
Construction Materials
6 4 3 2 2
Consumer Goods 13 0 6 2 1
Forestry 4 3 2 0 1
Fuel 4 4 4 0 2
Manufactured goods 8 10 5 1 1
Metro Containers 1 0 2 2 0
Minerals 2 0 2 0 1
Automotive 6 3 3 2 1
Oil Seeds 1 1 1 0 0
Steel 2 4 2 2 2
Waste 0 1 1 0 3
Other 7 0 0 1 0
I don't know 5 2 0 0 1
62 | P a g e
Table 4-8. Cross-tabulation between entity types and commodity groups, if the entity is a provider of goods
What sort of entity are you responding on behalf of?
Small business
Medium business
Large business
Industry Association
Other
Ple
ase
sp
ecif
y w
hic
h c
om
mo
dit
y gr
ou
ps
you
wo
rk w
ith
?
Agricultural Commodities
4 2 6 1 4
Coal 3 1 3 0 2
Construction Materials
5 3 4 1 2
Consumer Goods 12 1 7 1 2
Forestry 3 2 5 0 3
Fuel 3 3 5 0 3
Manufactured goods
8 9 8 0 2
Metro Containers 1 0 4 1 1
Minerals 1 0 5 0 1
Automotive 6 3 5 1 2
Oil Seeds 1 1 4 0 1
Steel 2 4 4 1 2
Waste 0 1 3 0 3
Other 7 1 0 1 0
I don't know 11 2 0 1 2
63 | P a g e
Table 4-9. Cross-tabulation between entity types and commodity groups If the entity is a carrier of goods
What sort of entity are you responding on behalf of?
Small business
Medium business
Large business
Industry Association
Other
Ple
ase
sp
ecif
y w
hic
h c
om
mo
dit
y gr
ou
ps
you
wo
rk w
ith
?
Agricultural Commodities
5 2 4 3 1
Coal 3 0 3 0 0
Construction Materials 7 4 5 2 0
Consumer Goods 11 0 4 3 0
Forestry 3 1 4 0 0
Fuel 3 2 3 0 0
Manufactured goods 6 9 7 2 0
Metro Containers 0 0 4 2 0
Minerals 1 0 4 0 0
Automotive 7 1 4 2 0
Oil Seeds 1 1 3 1 0
Steel 2 3 3 3 0
Waste 0 0 2 0 1
Other 4 0 0 1 0
I don't know 2 3 0 0 1
A.1.4. Transport modes
The following graphs show mode of transport used for moving cargo by different respondents.
Transport by road is the dominant mode of transport while the other three modes are relatively
equally used by the businesses of the sample (Figure 4-14).
64 | P a g e
Figure 4-14. Which mode of transport does your entity use to move the cargo?
Figure 4-15 cross-tabulates the modes of transport used by the respondents and their size. SBEs tend
to use road transport, while MBEs and LBEs also rely more on roads, but also rail and water. The
Industry Associations are distributed among all modes.
Figure 4-15. Cross-tabulation of mode of transport & entity type
Which mode of transport does your entity use to move the cargo
Count Highway /
Road Rail
Coast / Water
Air Other I don't know
Total
Wh
at s
ort
of
en
tity
are
yo
u
resp
on
din
g o
n b
ehal
f o
f?
Small business 43 15 5 19 5 12 99
Medium business 27 18 13 5 1 1 65
Large business 19 17 12 6 0 0 54
Industry Association 5 4 5 1 2 2 19
Other 3 1 2 1 2 3 12
Total 97 55 37 32 10 18 249
Figure 4-16 cross-tabulates the mode of transport and the frequency with which goods are
transported. Most respondents said that they use road transport more than 50 times per day; other
modes of transport are used less frequently.
39%
22%
15%
13%
4%
7%
Highway/Road
Rail
Marine/Water
Air
Other
I don't know
65 | P a g e
Figure 4-16. Cross-tabulation of mode of transport & the frequency of transport of goods
Which mode of transport does your entity use to move the cargo?
Count Highway /
Road Rail
Coast / Water
Air Other I don't know
Total
Ho
w o
fte
n d
oe
s yo
ur
en
tity
tra
nsp
ort
go
od
s vi
a th
ese
mo
de
s?
Less than once per month
0 1 1 2 1 5
Once per week 3 0 1 1 0 5
Once per day 0 1 1 1 0 3
Between 2 and 10 times a day
1 3 5 0 0 9
Between 10 than 50 times per day
2 5 3 0 0 10
More than 50 times per day
14 6 6 2 1 29
I don't know 2 2 2 2 3 11
Total 22 18 19 8 5 76 148
A.2. Data requirements
This section presents the detailed responses by survey respondents on the datasets used in their
entity, whether internally or externally sourced.
A.2.1. Data sourced internally
Starting with internally sourced datasets, Table 4-10 and Table 4-11 describe how these data are being
used by the respondents. The majority of the entities (67%) noted that they only deal with one
category of data. Among these entities mainly dealing with one type of data, the category
competitiveness is the most common followed by safety and performance of international gateways.
Table 4-10. Data sourced internally and its combination
Frequency Percent Valid percent
One category 99 67% 67%
Two categories 10 7% 74%
More than two categories 11 7% 81%
Missing 28 19% 100%
Total 148 100%
66 | P a g e
Table 4-11. Composition of data type sourced internally
Data category(s) Counts
On
e ca
tego
ry o
nly
Competitiveness 35
Performance of international gateways 16
Performance of multimodal networks 2
Infrastructure Performance 4
Safety 6
Regional freight 12
Urban Freight 5
Resilient freight 2
Mode-specific transport data 4
Other 13
Two
cat
ego
rie
s
Competitiveness & Performance of international gateways 1
Performance of international gateways & Infrastructure Performance 3
Performance of multimodal networks & Urban Freight 1
Safety & Regional freight 1
Regional freight & Mode-specific transport data 1
Urban Freight & Performance of international gateways 1
Performance of multimodal networks & other 1
Other & Other 1
Mo
re t
han
tw
o c
ate
gori
es
Competitiveness & Safety & Regional freight 1
Competitiveness & Performance of international gateways & Safety 1
Performance of international gateways & Safety & Mode-specific transport data
1
Performance of international gateways & Regional freight & Urban Freight 1
Infrastructure Performance & Safety & Mode-specific transport data 1
Competitiveness & Performance of multimodal networks & Infrastructure Performance & Safety & Regional freight
1
Infrastructure Performance & Safety & Regional freight & Urban Freight 1
Performance of international gateways & Regional freight & Urban Freight & Mode-specific transport data
1
Competitiveness & Performance of multimodal networks & Infrastructure Performance & Safety & Regional freight
1
Performance of international gateways & Infrastructure Performance & Safety & Mode-specific transport data & other
1
Competitiveness & Performance of multimodal networks & Infrastructure Performance & Safety & Regional freight &Urban Freight & Mode-specific transport data & Mode-specific transport data
1
67 | P a g e
Total 120
Further analysis of categories of internally used data is described in Figure 4-17. The category
competitiveness appears to be the most commonly used data sourced internally followed by
performance of international gateway (14.2%) and safety (12.3%).
Figure 4-17. Overal percent of data type sourced internally
The type of data being used is compared with the type of entity stating the data requirement to
provide more insights on the data usage of the respondents (Table 4-12). SBEs are mainly concerned
about the usage of competitiveness data sources, while MBEs work with the Performance of
multimodal networks datasets. LBEs seem to be using all types of data, sources internally, to some
24.2%
14.2%
6.2%
8.5%
12.3%
10.9%
5.2%
0.9%
4.7%
12.8%
Competitiveness
Performance of international gateways
Performance of multimodal networks
Infrastructure performance
Safety
Regional freight
Urban freight
Resilient freight
Mode-specific transport data
Other
68 | P a g e
extent but regional freight and safety related data category more. The small samples available of the
industry association are also interested to sources internally variety types of data categories.
69 | P a g e
Table 4-12. Cross-tabulation between type of entity & data category sourced internally
Data type sourced internally
Counts Competitiveness
Performance
of
international
gateways
Performance
of multimodal
networks
Infrastructure
performance Safety
Regional
freight
Urban
freight
Resilient
freight
Mode-
specific
transport
data
Other Total
Wh
at s
ort
of
en
tity
are
you
res
po
nd
ing
on
beh
alf
of?
Small business 26 13 3 9 8 2 5 1 1 6 74
Medium business 18 8 3 3 5 5 2 1 2 1 48
Large business 5 9 5 4 10 11 2 0 4 5 55
Industry
Association 2 0 2 1 2 2 1 0 2 2 14
Other 0 0 0 1 1 3 1 0 1 13 20
Total 51 30 13 18 26 23 11 2 10 27 211
70 | P a g e
Several subcategories are provided for the major data categories discussed earlier in the previous
figures and tables. Table 4-13 shows the further breakdown of internally used datasets based on
respondents’ answers. Labour and market comparison are the dominating subcategories of the
competitiveness category which is used commonly by companies, sourced internally. The safety
category does not have a dominant subcategory, while the performance of international gateways
appears to be further reflected under the best practice modelling assumptions and the value of freight
to the national economy. Further, rail, road, first mile access metrics, remote metrics for Northern
Australia and weather are the least frequent subcategories in the reported data types.
71 | P a g e
Table 4-13. Cross-tabulation between data category & subcategory sourced internally
Data Subcategory
Counts
Lab
ou
r
Val
ue
of
frei
ght
to
the
nat
ion
al
eco
no
my
Po
rts
Air
po
rts
Cu
sto
ms
Frei
ght
Dat
a
An
alys
is P
roje
ct
Net
wo
rk
Op
tim
isat
ion
Fram
ewo
rks
Bes
t P
ract
ice
Mo
del
ling
Ass
um
pti
on
s
Ro
ads,
tra
cks,
bri
dge
s, t
un
ne
ls
Ro
ad
Vo
lum
es
Firs
t m
ile a
cces
s
Last
mile
per
form
ance
met
rics
Lan
d s
up
ply
an
d
con
flic
t
Lan
dsi
de
logi
stic
s
cost
s
Co
nge
stio
n m
etr
ics
Rem
ote
met
rics
fo
r
No
rth
ern
Au
stra
lia
Rai
l
Fore
cast
ing
and
pro
ject
ion
Tim
esta
mp
Mar
ket
com
par
iso
n
Wea
ther
Oth
er
E-co
mm
erce
Tota
l
Dat
a ca
tego
ry
Competitiveness 10 4 2 2 0 0 1 8 4 5 0 1 0 1 0 0 1 3 0 4 0 0 5
51
Performance of international gateways
2 4 5 1 2 1 3 0 1 3 1 1 0 4 0 0 0 0 1 0 0 0 1
30
Performance of multimodal networks
0 2 2 1 0 2 0 1 0 0 1 0 0 2 0 0 1 0 0 0 0 1 0
13
Infrastructure performance
1 3 0 2 0 1 0 1 3 3 0 0 0 1 0 0 1 1 0 0 1 0 0
18
Safety 5 0 3 0 0 1 3 2 3 2 2 0 1 0 0 0 1 0 0 1 0 2 0
26
Regional freight 1 0 1 0 1 2 0 1 0 6 0 2 1 4 1 0 1 0 1 0 0 1 0
23
Urban freight 1 0 2 0 0 0 0 0 2 2 0 1 0 2 0 0 0 0 0 1 0 0 0
11
Resilient freight 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
2
Mode-specific transport data
0 0 2 0 0 2 1 0 1 1 0 0 0 0 0 0 2 0 0 1 0 0 0
10
Other 1 0 3 1 1 0 1 0 0 1 0 0 0 0 0 1 0 1 1 1 0 15 0
27
Total 21 14 20 7 4 9 9 13 14 23 4 5 2 14 2 1 7 5 3 8 1 19 6
211
72 | P a g e
Table 4-14 shows what type of data is used for what purpose. Operation, as the most commonly
indicated purpose of use for internally sourced data, is mainly related to competitiveness and
performance of international gateways. The planning purpose, however, has a major concentration
on competitiveness followed by infrastructure performance and safety. When all three usage purposes
are considered (last column), mode specific transport data becomes critical, although this category
has a small overall proportion among all data types.
A critical concern of all companies, specifically about the data sourced internally is whether the data
can be shared with others. Almost two-thirds of respondents stated that their data can be shared to
some extent, whereas one-fifth stated that their data can become publicly available. The breakdown
across different data types (Table 4-15) reveals that when data cannot be shared is mainly used for
competitiveness and safety. Performance of international gateways and infrastructure performance
are the two categories having a wide range of concerns with regard to data sharing.
To better understand the distribution of data types used by companies, the cross tabulations
presented previously are further classified based on entity types. Table 4-16 shows the distribution of
different data types of SBEs. SBEs are mainly using data in the category of competitiveness which can
be mainly broken down to the labour subcategory.
When focusing on distribution of purpose and data types for SBEs, when compared to all entities
(Table 4-17), SBEs are more focused on planning than operation where the distribution of data
categories is relatively evenly distributed for the operation category. However, when it comes to data
sharing, smaller companies show a greater reluctance, as they are more sensitive to their
competitiveness with their counterparts (Table 4-18).
Unlike the small sized companies, the internally sourced data for MBEs is mainly used for operation
than planning. As Table 4-19 shows, competitiveness is not the dominating data category for medium
sized companies. Table 4-21 shows that MBEs seem to be quite receptive to share their data, and
when they are not, they seem to be concerned about data falling into the safety category.
Table 4-22 shows the distribution of data categories and subcategories for LBEs. LBEs (like MBEs) are
more interested in operation purposes with a major difference that they consider data for more types
of purposes when using their internally sourced databases. Unlike the MBEs, LBEs participated in this
survey appear to be concerned about sharing their internally sourced data. Even when they are happy
to share their data, they prefer to make it publicly available or share it to government agencies
compared to other types of agencies (Table 4-24).
Table 4-25 through to Table 4-27 discuss the responses of Industry Association (IA) entities. With
regard to the type of data they use, volume, first mile, lands and logistics costs, remote metrics for
Northern Australia and market comparison are the only subcategories identified by the respondents
of this type (Table 4-25). IA entities appear to be more interested in using their internally sourced data
for multiple purposes, especially for all three categories of planning, investment and operation (Table
4-26). Compared to all the other types of companies, IA entities seem to be extremely sensitive in
sharing their internally sourced data, regardless of the data type (Table 4-27).
73 | P a g e
Table 4-14. Cross-tabulation between data category & purpose of use for data sourced internally
Data Purpose
Counts Planning Operation Investment
Planning
and
operation
Planning and
investment
Operation
and
investment
Planning,
operation
and
investment
Total
Dat
a ca
tego
ry
Competitiveness 12 13 11 7 4 1 3 51
Performance of international gateways 3 10 4 7 1 0 5 30
Performance of multimodal networks 0 1 1 5 2 0 4 13
Infrastructure performance 4 4 5 1 3 0 1 18
Safety 4 8 2 6 0 3 3 26
Regional freight 2 3 2 4 2 3 7 23
Urban freight 3 3 3 1 0 1 0 11
Resilient freight 0 2 0 0 0 0 0 2
Mode-specific transport data 2 0 0 1 1 1 5 10
Other 13 4 1 3 1 0 5 27
Total 43 48 29 35 14 9 33 211
74 | P a g e
Table 4-15. Cross-tabulation between data category & if the data could be shared, for sourced internally
Can this data be shared?
Counts Yes, publicly to
anyone
Yes, to any
government agency
or department
Yes, to non-
government entities
Yes, to government
agency with structural
independence
No, the data
cannot be
shared with
anyone at all
Total
Dat
a ca
tego
ry
Competitiveness 26 8 4 1 12 51
Performance of
international gateways 4 10 7 6 3 30
Performance of multimodal
networks 4 2 2 3 2 13
Infrastructure performance 4 6 2 2 4 18
Safety 3 5 1 3 14 26
Regional freight 4 5 1 5 8 23
Urban freight 2 2 0 2 5 11
Resilient freight 0 2 0 0 0 2
Mode-specific transport
data 1 4 0 1 4 10
Other 5 2 2 1 17 27
Total 21 53 46 19 24 69
75 | P a g e
Table 4-16. Cross-tabulation between data category sourced internally & subcategory for SBEs
Data Subcategory
Counts La
bo
ur
Val
ue
of
frei
ght
to t
he
nat
ion
al e
con
om
y
Po
rts
Air
po
rts
Cu
sto
ms
Frei
ght
Dat
a
An
alys
is P
roje
ct
Net
wo
rk O
pti
mis
atio
n
Fram
ewo
rks
Bes
t P
ract
ice
Mo
del
ling
Ass
um
pti
on
s
Ro
ads,
tra
cks,
bri
dge
s,
tun
nel
s
Ro
ad
Vo
lum
es
Firs
t m
ile a
cces
s
Last
mile
per
form
ance
met
rics
Lan
dsi
de
logi
stic
s co
sts
Co
nge
stio
n m
etr
ics
Fore
cast
ing
and
pro
ject
ion
Tim
esta
mp
Mar
ket
com
par
iso
n
Wea
ther
Oth
er
E-co
mm
erce
Total
Dat
a ca
tego
ry
Competitiveness 7 4 0 1 0 0 0 0 2 2 0 1 0 0 2 0 2 0
0 5 26
Performance of international gateways
2 3 1 0 1 1 1 0 0 1 1 0 1 0 0 0 0 0
0 1 13
Performance of multimodal networks
0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
0 0 3
Infrastructure performance
1 1 0 1 0 1 0 1 2 0 0 0 0 0 1 0 0 1
0 0 9
Safety 0 0 0 0 0 0 3 1 1 0 1 0 0 0 0 0 1 0
1 0 8
Regional freight 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 2
Urban freight 1 0 0 0 0 0 0 0 2 1 0 0 1 0 0 0 0 0
0 0 5
Resilient freight 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
0 0 1
Mode-specific transport data
0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
0 0 1
Other 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0
2 0 6
Total 13 9 2 3 1 3 5 3 7 4 2 1 2 1 3 2 3 1
3 6 74
76 | P a g e
Table 4-17. Cross-tabulation between data category sourced internally & purpose for SBEs
Data Purpose
Counts Planning Operation Investment Planning
and operation
Planning and investment
Operation and
investment
Planning, operation
and investment
Total
Dat
a ca
tego
ry
Competitiveness 8 9 3 4 0 1 1 26
Performance of international gateways
3 4 1 4 1 0 0 13
Performance of multimodal networks
0 1 0 1 1 0 0 3
Infrastructure performance 3 2 2 0 2 0 0 9
Safety 3 2 1 1 0 1 0 8
Regional freight 0 1 0 0 0 1 0 2
Urban freight 2 1 2 0 0 0 0 5
Resilient freight 0 1 0 0 0 0 0 1
Mode-specific transport data 0 0 0 0 1 0 0 1
Other 3 2 0 0 0 0 1 6
Total 22 23 9 10 5 3 2 74
77 | P a g e
Table 4-18. Cross-tabulation between data category sourced internally & if the data can be shared for SBEs
Can this data be shared?
Counts Yes, publicly
to anyone Yes, to any government agency or department
Yes, to non-government
entities
Yes, to government agency with
structural independence
No, the data cannot be shared with anyone at all
Total
Dat
a ca
tego
ry
Competitiveness 12 3 2 0 9 26
Performance of international gateways
3 6 3 0 1 13
Performance of multimodal networks
1 1 1 0 0 3
Infrastructure performance 2 4 1 1 1 9
Safety 0 1 1 2 4 8
Regional freight 0 0 0 0 2 2
Urban freight 1 0 0 1 3 5
Resilient freight 0 1 0 0 0 1
Mode-specific transport data 0 1 0 0 0 1
Other 2 1 0 0 3 6
78 | P a g e
Total 21 18 8 4 23 74
79 | P a g e
Table 4-19. Cross-tabulation between data category sourced internally & subcategory for MBEs
Data Subcategory
Counts
Lab
ou
r
Val
ue
of
frei
ght
to t
he
nat
ion
al e
con
om
y
Po
rts
Air
po
rts
Cu
sto
ms
Frei
ght
Dat
a
An
alys
is P
roje
ct
Net
wo
rk O
pti
mis
atio
n
Fram
ewo
rks
Bes
t P
ract
ice
Mo
del
ling
Ass
um
pti
on
s
Ro
ads,
tra
cks,
bri
dge
s,
tun
nel
s
Ro
ad
Vo
lum
es
Lan
d s
up
ply
an
d c
on
flic
t
Lan
dsi
de
logi
stic
s co
sts
Co
nge
stio
n m
etr
ics
Rai
l
Fore
cast
ing
and
pro
ject
ion
Mar
ket
com
par
iso
n
Total
Dat
a ca
tego
ry
Competitiveness 2 0 1 1 0 0 1 8 2 1 0 0 0 1 1 0 18
Performance of international gateways
0 1 2 1 0 0 2 0 1 0 0 1 0 0 0 0 8
Performance of multimodal networks
0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 3
Infrastructure performance
0 2 0 0 0 0 0 0 0 1 0 0 0 0 0 0 3
Safety 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 5
Regional freight 0 0 0 0 1 1 0 0 0 1 0 1 1 0 0 0 5
Urban freight 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 2
Resilient freight 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Mode-specific transport data
0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 2
Other 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1
80 | P a g e
Total 3 5 6 3 1 2 4 9 4 4 1 2 1 1 1 1 48
81 | P a g e
Table 4-20. Cross-tabulation between data category sourced internally & purpose for MBEs
Data Purpose
Counts Planning Operation Investment Planning
and operation
Planning and
investment
Operation and
investment
Planning, operation
and investment
Total
Dat
a ca
tego
ry
Competitiveness 3 4 8 2 1 0 0 18
Performance of international gateways 0 4 2 2 0 0 0 8
Performance of multimodal networks 0 0 0 2 1 0 0 3
Infrastructure performance 1 1 1 0 0 0 0 3
Safety 0 0 1 3 0 1 0 5
Regional freight 1 0 1 2 1 0 0 5
Urban freight 1 1 0 0 0 0 0 2
Resilient freight 0 1 0 0 0 0 0 1
Mode-specific transport data 1 0 0 0 0 0 1 2
Other 0 0 0 0 0 0 1 1
Total 7 11 13 11 3 1 2 48
82 | P a g e
Table 4-21. Cross-tabulation between data category sourced internally & if the data can be shared for MBEs
Can this data be shared?
Counts Yes, publicly to
anyone
Yes, to any government agency or
department
Yes, to non-government
entities
Yes, to government agency with structural
independence
No, the data cannot be
shared with anyone at all
Total
Data
ca
teg
ory
Competitiveness 13 2 2 0 1 18
Performance of international gateways
1 3 2 2 0 8
Performance of multimodal networks
2 0 0 1 0 3
Infrastructure performance
1 0 1 0 1 3
Safety 2 1 0 0 2 5
Regional freight 0 2 1 2 0 5
Urban freight 0 1 0 0 1 2
Resilient freight 0 1 0 0 0 1
Mode-specific transport data
0 2 0 0 0 2
Other 1 0 0 0 0 1
Total 20 12 6 5 5 48
83 | P a g e
Table 4-22. Cross-tabulation between data category sourced internally & subcategory for LBEs
Data Subcategory
Count La
bo
ur
Po
rts
Air
po
rts
Cu
sto
ms
Frei
ght
Dat
a
An
alys
is P
roje
ct
Net
wo
rk
Op
tim
isat
ion
Fram
ewo
rks
Ro
ad
Vo
lum
es
Firs
t m
ile a
cces
s
Last
mile
per
form
ance
met
rics
Lan
d s
up
ply
an
d
con
flic
t
Lan
dsi
de
logi
stic
s co
sts
Rai
l
Fore
cast
ing
and
pro
ject
ion
Tim
esta
mp
Mar
ket
com
par
iso
n
Oth
er
Total
Dat
a ca
tego
ry
Competitiveness 1 1 0 0 0 0 1 0 0 0 0 0 0 0 2 0 5
Performance of international gateways
0 2 0 1 0 0 2 0 1 0 2 0 0 1 0 0 9
Performance of multimodal networks
0 1 0 0 1 0 0 0 0 0 1 1 0 0 0 1 5
Infrastructure performance
0 0 1 0 0 1 1 0 0 0 0 1 0 0 0 0 4
Safety 4 2 0 0 1 0 0 1 0 0 0 1 0 0 0 1 10
Regional freight 0 1 0 0 0 0 4 0 2 1 2 1 0 0 0 0 11
Urban freight 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 2
Mode-specific transport data
0 1 0 0 1 1 0 0 0 0 0 1 0 0 0 0 4
Other 0 2 0 1 0 0 1 0 0 0 0 0 1 0 0 0 5
84 | P a g e
Total 5 10 1 2 3 2 10 1 4 1 5 5 1 1 2 2 55
Table 4-23. Cross-tabulation between data category sourced internally & purpose for LBEs
Data Purpose
Count Planning Operation Investment Planning and
operation Planning and investment
Operation and investment
Planning, operation and
investment Total
Dat
a ca
tego
ry
Competitiveness 1 0 0 1 1 0 2 5
Performance of international gateways
0 2 1 1 0 0 5 9
Performance of multimodal networks
0 0 1 0 0 0 4 5
Infrastructure performance 0 1 1 1 0 0 1 4
Safety 1 4 0 1 0 1 3 10
Regional freight 0 2 0 2 0 1 6 11
Urban freight 0 1 0 1 0 0 0 2
Mode-specific transport data
1 0 0 1 0 1 1 4
85 | P a g e
Other 0 0 0 3 0 0 2 5
Total 3 10 3 11 1 3 24 55
86 | P a g e
Table 4-24. Data category (Internal) * Can this data be shared (Internal) Cross-tabulation – LBEs
Can this data be shared?
Counts Yes, publicly
to anyone
Yes, to any government
agency or department
Yes, to non-government
entities
Yes, to government agency with
structural independence
No, the data cannot be
shared with anyone at all
Total
Dat
a ca
tego
ry
Competitiveness 1 2 0 1 1 5
Performance of international gateways 0 1 2 4 2 9
Performance of multimodal networks 1 1 0 2 1 5
Infrastructure performance 1 2 0 1 0 4
Safety 1 3 0 1 5 10
Regional freight 1 3 0 2 5 11
Urban freight 0 1 0 1 0 2
Mode-specific transport data 0 1 0 0 3 4
Other 2 1 0 0 2 5
Total 7 15 2 12 19 55
87 | P a g e
Table 4-25. Cross-tabulation between data category sourced internally & subcategory - IAs
Data Subcategory
Counts
Ro
ad
Vo
lum
es
Firs
t m
ile a
cces
s
Lan
dsi
de
logi
stic
s
cost
s
Rem
ote
met
rics
fo
r N
ort
her
n
Au
stra
lia
Mar
ket
com
par
iso
n
Oth
er
Tota
l
Dat
a ca
tego
ry
Competitiveness 0 1 0 1 0 0 0 2
Performance of multimodal networks
0 0 1 1 0 0 0 2
Infrastructure performance 0 0 0 1 0 0 0 1
Safety 1 1 0 0 0 0 0 2
Regional freight 0 0 0 1 0 0 1 2
Urban freight 0 0 0 1 0 0 0 1
Mode-specific transport data
0 1 0 0 0 1 0 2
Other 0 0 0 0 1 0 1 2
Total 1 3 1 5 1 1 2 14
88 | P a g e
Table 4-26. Cross-tabulation between data category sourced internally & purpose - IAs
Data Purpose
Counts Operation Investment Planning and
operation Planning and investment
Operation and investment
Planning, operation and
investment Total
Dat
a ca
tego
ry
Competitiveness 0 0 0 2 0 0 2
Performance of multimodal networks 0 0 2 0 0 0 2
Infrastructure performance 0 0 0 1 0 0 1
Safety 1 0 1 0 0 0 2
Regional freight 0 0 0 0 1 1 2
Urban freight 0 0 0 0 1 0 1
Mode-specific transport data 0 0 0 0 0 2 2
Other 1 1 0 0 0 0 2
Total 2 1 3 3 2 3 14
89 | P a g e
Table 4-27. Cross-tabulation between data category sourced internally & if the data can be shared - IAs
Can this data be shared?
Counts Yes, publicly to
anyone
Yes, to any government agency
or department
Yes, to non-government entities
Yes, to government agency with structural
independence
No, the data cannot be
shared with anyone at all
Total
Dat
a ca
tego
ry
Competitiveness 0 1 0 0 1 2
Performance of multimodal networks
0 0 1 0 1 2
Infrastructure performance
0 0 0 0 1 1
Safety 0 0 0 0 2 2
Regional freight 0 0 0 1 1 2
Urban freight 0 0 0 0 1 1
Mode-specific transport data
1 0 0 0 1 2
Other 0 0 1 0 1 2
Total 1 1 2 1 9 14
90 | P a g e
A.2.2. Data sourced externally
Most respondents stated that they primarily use only one category of data (Table 4-28).
Table 4-28. Data sourced externally and its combination
Frequency Percent
Valid
percent
One category 58 39% 39%
Two categories 6 4% 43%
More than two categories 11 7% 51%
Missing 73 49%
Total 148 100% 100%
Competitiveness, performance of international gateways, safety, and competitiveness were found to
be the most common types of data used by entities, as external data (Table 4-29).
Table 4-29. Composition of data type sourced externally Data category(s) Count
On
e ca
tego
ry o
nly
Competitiveness 11 Performance of international gateways 8 Performance of multimodal networks 2 Infrastructure Performance 5 Safety 8 Regional freight 7 Urban Freight 5 Resilient freight 1 Mode-specific transport data 7 other 4
Two
cat
ego
ries
Competitiveness & Performance of international gateways 1 Performance of international gateways & Infrastructure Performance 1 Safety & Regional freight 1 Regional freight & Urban Freight 1 Performance of multimodal networks & Mode-specific transport data 1 Performance of multimodal networks & Other 1
Mo
re t
han
tw
o c
ate
gori
es Competitiveness & Performance of multimodal networks & Resilient freight 1
Performance of multimodal networks & Infrastructure Performance & Mode-specific transport data
1 Infrastructure Performance & Regional freight & Urban Freight 1 Safety & Urban freight & Regional freight 1 Safety & Regional freight & Mode-specific transport data 1 Performance of international gateways & Performance of multimodal networks & Infrastructure Performance & Mode-specific transport data
1 Safety & Performance of multimodal networks & Mode-specific transport data & Infrastructure Performance
1 Competitiveness & Performance of international gateways & Infrastructure Performance & Regional freight & Resilient freight
1 Performance of multimodal networks & Regional freight & Urban Freight & Mode-specific transport data & other
1 Competitiveness & Performance of multimodal networks & Infrastructure Performance & Safety & Regional freight & Urban Freight & Mode-specific transport data
1 All data categories 1
Total 75
91 | P a g e
The overall distribution of different data types, presented in Figure 4-18, conform to some extent with
what was observed for the internally sourced data. The top noted categories are competitiveness
(20%), safety (16%), and mode-specific transport data (13.3%) and performance of infrastructure
(13.3%).
Figure 4-18. Overal percent of data type sourced externally
When the size of the entity and the type of data being used is of interest, competitiveness ranks highly
for SBEs and MBEs (Table 4-30), safety is only noted by SBEs. LBEs appear to be interested in a broad
range of issues, including mode-specific transport data and performance of international gateways.
20.0%
13.3%
4.0%
6.7%
16.0%
12.0%
6.7%
1.3%
13.3%
6.7%
Competitiveness
Performance of internationalgateways
Performance of multimodalnetworks
Infrastructure performance
Safety
Regional freight
Urban freight
Resilient freight
Mode-specific transport data
Other
92 | P a g e
Table 4-30. Cross-tabulation between the type of entity & data category sourced externally
Data type
Counts Competitiveness
Performance of
international gateways
Performance of
multimodal networks
Infrastructure performance
Safety Regional freight
Urban freight
Resilient freight
Mode-specific
transport data
Other Total
Wh
at s
ort
of
en
tity
are
yo
u
resp
on
din
g o
n b
ehal
f o
f?
Small business
8 4 4 5 8 5 3 1 1 2 41
Medium business
13 2 1 3 0 3 2 0 5 0 29
Large business
1 4 2 2 1 4 2 1 6 2 25
Industry Association
3 3 2 2 5 3 3 1 3 1 26
Total 1 1 1 1 1 2 2 1 3 2 15
93 | P a g e
Table 4-31 shows the detailed breakdown of data categories among different data subcategories.
Labour and market condition are not the dominating subcategories, while volumes appear to be most
dominating type of data being sourced externally for usage by the respondents. Although the sample
is relatively small for companies reported externally sourced data being used by them, still all
subcategories have at least one company being interested in having access to such data.
94 | P a g e
Table 4-31. Cross-tabulation between data category & subcategory sourced externally
Data Subcategory
Counts La
bo
ur
Val
ue
of
frei
ght
to t
he
nat
ion
al e
con
om
y
Po
rts
Air
po
rts
Net
wo
rk O
pti
mis
atio
n
Fram
ewo
rks
Bes
t P
ract
ice
Mo
del
ling
Ass
um
pti
on
s
Ro
ads,
tra
cks,
bri
dge
s,
tun
nel
s
Ro
ad
Vo
lum
es
Firs
t m
ile a
cces
s
Last
mile
per
form
ance
met
rics
Lan
d s
up
ply
an
d
con
flic
t
Lan
dsi
de
logi
stic
s co
sts
Co
nge
stio
n m
etr
ics
Rem
ote
met
rics
fo
r
No
rth
ern
Au
stra
lia
Rai
l
Fore
cast
ing
and
pro
ject
ion
Tim
esta
mp
Mar
ket
com
par
iso
n
Wea
ther
Oth
er
E-co
mm
erce
Tota
l
Dat
a ca
tego
ry
Competitiveness 12 2 2 0 1 0 0 1 1 0 0 1 1 2 0 0 0 0 0 0 0 3 26
Performance of international gateways
0 1 3 1 0 0 0 1 4 0 0 1 0 0 1 2 0 0 0 0 0 0 14
Performance of multimodal networks
0 0 1 0 0 0 0 0 1 0 0 0 3 2 0 1 0 0 1 0 1 0 10
Infrastructure performance 0 1 2 0 1 0 2 0 0 0 2 0 3 1 0 0 0 0 0 1 0 0 13
Safety 0 0 0 2 0 0 0 4 4 0 0 1 0 0 0 1 0 0 0 1 2 0 15
Regional freight 0 1 0 0 3 0 1 1 1 1 2 0 4 1 0 0 0 1 0 0 1 0 17
Urban freight 0 1 1 0 0 0 0 0 2 0 1 1 3 1 0 0 0 2 0 0 0 0 12
Resilient freight 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4
Mode-specific transport data 0 0 3 0 0 1 0 3 3 0 2 0 1 0 1 2 0 0 1 0 0 1 18
Other 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 0 7
95 | P a g e
Total 14 7 13 5 6 1 4 10 16 1 7 4 15 7 2 6 1 3 2 2 6 4 136
96 | P a g e
The externally sourced databases are only used for one purpose (Table 4-32. Cross-tabulation
between data category & purpose of use for data sourced externally., ie. no multiple purposes are
reported in the data, where planning is the most commonly considered purposes across all data types,
while operation is primarily considered if the data type being used is competitiveness, safety, or
performance of international gateways. Surprisingly, the investment purpose is seldom noted by the
respondents as the main purpose of using externally sourced data.
Table 4-32. Cross-tabulation between data category & purpose of use for data sourced externally
Data Purpose
Counts Planning Operation Investment Total
Dat
a ca
tego
ry
Competitiveness 7 17 2 26
Performance of international gateways 9 4 1 14
Performance of multimodal networks 6 3 1 10
Infrastructure performance 8 2 3 13
Safety 6 9 0 15
Regional freight 7 8 2 17
Urban freight 5 4 3 12
Resilient freight 2 1 1 4
Mode-specific transport data 11 5 2 18
Other 5 2 0 7
Total 66 55 15 136
A new piece of information is provided for the externally sourced data which is about the frequency
of usage. Table 4-33 shows the distribution of the frequency use of the data based on the type of data
for all respondents. Almost all data types have been reported to be used by a few companies on daily
basis. As the distribution of data in Table 4-33 is not skewed toward any side of the table, almost half
of the data records are referring to data being used less frequent than once per month. This finding is
clearer for mode specific data types as well as the safety category.
97 | P a g e
Table 4-33. Cross-tabulation between data category & the frequency of used, for sourced externally
Frequency of use
Counts Every day
Two to three
times a week
Once a week
Twice a month
Once a month
Every three
months
Every six months
Every year or more
Total
Dat
a ca
tego
ry
Competitiveness 4 2 7 0 9 1 1 2 26
Performance of international gateways
0 3 4 1 5 0 0 1 14
Performance of multimodal networks
2 2 1 1 3 0 0 1 10
Infrastructure performance 1 3 3 0 3 2 0 1 13
Safety 1 2 2 0 3 4 0 3 15
Regional freight 4 2 2 1 4 0 1 3 17
Urban freight 3 0 1 0 5 1 1 1 12
Resilient freight 1 0 0 0 2 0 0 1 4
Mode-specific transport data 6 1 0 0 4 2 2 3 18
Other 0 0 1 0 4 0 0 2 7
Total 22 15 21 3 42 10 5 18 136
98 | P a g e
The cost of accessing to the reported externally sourced databases appears to be mainly less than
$1,000, unless it is related to performance of multimodal networks, which is skewed toward the $1,000
to $9,9999 category (Table 4-34). There are only 6 responses referring to the instance of externally
sourced data that cost more than $10,000.
Table 4-34. Cross-tabulation between data category & the cost to access, for sourced externally
Cost to access data
Counts Less than
$1,000 $1,000 - $9,999
$10,000 or more
Total
Dat
a ca
tego
ry
Competitiveness 20 2 2 24
Performance of international gateways
10 3 1 14
Performance of multimodal networks
8 3 0 11
Infrastructure performance 10 4 0 14
Safety 12 2 1 15
Regional freight 13 3 1 17
Urban freight 8 2 2 12
Resilient freight 3 0 1 4
Mode-specific transport data 13 1 4 18
Other 5 0 2 7
Total 102 20 14 136
Like the analysis of the internally sourced data, we focus more on the impact of size of the component
on the type of data being used and externally sourced. Table 4-35 shows the distribution different
data categories and subcategories. Iven the small sample size such distribution does not reveal a trend,
nonetheless, it can still be seen that the volume and safety are considered by the smaller companies.
99 | P a g e
Table 4-35. Cross-tabulation between data category sourced externally & subcategory, for SBEs
Data Subcategory
Counts
Lab
ou
r
Val
ue
of
frei
ght
to
the
nat
ion
al
eco
no
my
Po
rts
Air
po
rts
Net
wo
rk
Op
tim
isat
ion
Fram
ewo
rks
Ro
ads,
tra
cks,
b
rid
ges,
tu
nn
els
Ro
ad
Vo
lum
es
Last
mile
per
form
ance
met
rics
Lan
d s
up
ply
an
d
con
flic
t
Lan
dsi
de
logi
stic
s
cost
s
Co
nge
stio
n m
etr
ics
Rai
l
Tim
esta
mp
Mar
ket
com
par
iso
n
Wea
ther
Oth
er
E-co
mm
erce
Tota
l
Dat
a ca
tego
ry
Competitiveness 1 0 0 0 1 0 1 0 0 0 0 2 0 0 0 0 0 3 8
Performance of international gateways
0 1 0 0 0 0 0 2 0 1 0 0 0 0 0 0 0 0 4
Performance of multimodal networks
0 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 4
Infrastructure performance 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 5
Safety 0 0 0 2 0 0 1 3 0 0 0 0 1 0 0 1 0 0 8
Regional freight 0 1 0 0 0 0 1 0 1 0 0 1 0 1 0 0 0 0 5
Urban freight 0 0 0 0 0 0 0 0 0 0 2 0 0 1 0 0 0 0 3
Resilient freight 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Mode-specific transport data 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
Other 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 2
100 | P a g e
Total 2 2 2 3 1 1 3 6 2 1 2 5 1 2 1 2 1 4 41
101 | P a g e
Like what was observed for the internally sourced data, SBEs are focused on using the data for
planning and operation purposes, however the focus on planning is less strong for the externally
sourced data (Table 4-36).
Table 4-36. Cross-tabulation between data category sourced externally & purpose for SBEs
Data Purpose
Counts Planning Operation Investment Total
Dat
a ca
tego
ry
Competitiveness 1 7 0 8
Performance of international gateways
2 2 0 4
Performance of multimodal networks
2 2 0 4
Infrastructure performance 3 1 1 5
Safety 3 5 0 8
Regional freight 1 4 0 5
Urban freight 2 1 0 3
Resilient freight 1 0 0 1
Mode-specific transport data 0 1 0 1
Other 1 1 0 2
Total 16 24 1 41
The frequency of usage of externally sourced data for smaller companies is very high where very few
responses have provided for using any data types for less frequent than once per month (Table 4-37).
Those instances of using the data for less than once a month are observed for the safety and
infrastructure performance.
102 | P a g e
Table 4-37. Cross-tabulation between data category sourced externally & frequency of use, for SBEs
Frequency of use
Counts Every day
Two to three
times a week
Once a week
Twice a month
Once a month
Every three
months
Every six months
Every year or more
Total
Dat
a ca
tego
ry
Competitiveness 3 1 3 0 1 0 0 0 8
Performance of international gateways
0 1 2 1 0 0 0 0 4
Performance of multimodal networks
1 2 0 1 0 0 0 0 4
Infrastructure performance 0 0 2 0 1 2 0 0 5
Safety 1 2 1 0 2 1 0 1 8
Regional freight 0 1 1 1 0 0 1 1 5
Urban freight 0 0 0 0 2 1 0 0 3
Resilient freight 1 0 0 0 0 0 0 0 1
Mode-specific transport data 0 0 0 0 1 0 0 0 1
Other 0 0 0 0 1 0 0 1 2
103 | P a g e
Total 6 7 9 3 8 4 1 3 41
104 | P a g e
Table 4-38 shows that SBEs are willing to purchase data for values higher than $1,000, especially if it
is related to the performance of the system.
Table 4-38. Cross-tabulation between data category sourced externally & cost of access, for SBEs
Cost to access data
Less than
$1,000 $1,000 - $9,999
$10,000 or more
Total
Dat
a ca
tego
ry
Competitiveness 5 1 2 8
Performance of international gateways
2 2 0 4
Performance of multimodal networks
1 3 0 4
Infrastructure performance 2 3 0 5
Safety 5 2 1 8
Regional freight 4 1 0 5
Urban freight 2 1 0 3
Resilient freight 0 0 1 1
Mode-specific transport data 1 0 0 1
Other 2 0 0 2
Total 24 13 4 41
Data for MBEs is limited to almost half of the data categories (Table 4-39). Competitiveness is the
dominant category of interest.
105 | P a g e
Table 4-39. Cross-tabulation between data category sourced externally & subcategory, for MBEs
Data Subcategory
Counts La
bo
ur
Val
ue
of
frei
ght
to
the
nat
ion
al
eco
no
my
Po
rts
Net
wo
rk
Op
tim
isat
ion
Fr
amew
ork
s R
oad
s, t
rack
s,
bri
dge
s, t
un
ne
ls
Ro
ad
Vo
lum
es
Firs
t m
ile a
cces
s
Last
mile
per
form
ance
met
rics
La
nd
sid
e lo
gist
ics
cost
s
Co
nge
stio
n
met
rics
Rem
ote
met
rics
fo
r
No
rth
ern
Au
stra
lia
Ra
il
Tim
esta
mp
Tota
l
Dat
a ca
tego
ry
Competitiveness 11 1 0 0 0 0 1 0 0 0 0 0 0 0 13
Performance of international
gateways
0 0 0 0 0 0 1 0 0 0 0 1 0 0 2
Performance of multimodal networks
0 0 0 0 0 0 0 0 0 0 1 0 0 0 1
Infrastructure performance
0 1 0 0 0 0 0 0 1 1 0 0 0 0 3
Regional freight 0 0 0 1 1 0 0 1 0 0 0 0 0 0 3
Urban freight 0 1 0 0 0 0 0 0 0 0 0 0 0 1 2
Mode-specific transport data
0 0 2 0 0 1 0 0 0 0 0 1 1 0 5
Total 11 3 2 1 1 1 2 1 1 1 1 2 1 1 29
Operation is the main purpose for purchasing externally sourced data for MBEs, which was the case
for internally sourced data as well (Table 4-40). Investment and planning are also important for MBEs,
where planning is related to performance related categories, and investment pertains to freight
related categories.
106 | P a g e
Table 4-40. Cross-tabulation between data category sourced externally & purpose, for MBEs
Data Purpose
Counts Planning Operation Investment Total
Dat
a ca
tego
ry
Competitiveness 4 9 0 13
Performance of international gateways
1 1 0 2
Performance of multimodal networks
1 0 0 1
Infrastructure performance
3 0 0 3
Regional freight 0 2 1 3
Urban freight 0 0 2 2
Mode-specific transport data
3 2 0 5
Total 12 14 3 29
As is the case for SBEs, the data that is used by MBEs is used frequently, as seen in Table 4-41.
Table 4-41. Cross-tabulation between data category sourced externally & frequency of use, for MBEs
Frequency of use
Counts Every day
Two to three
times a week
Once a week
Once a month
Every three
months
Every six months
Every year or more
Total
Dat
a ca
tego
ry
Competitiveness 0 1 4 6 1 1 0 13
Performance of international gateways
0 1 1 0 0 0 0 2
Performance of multimodal networks
0 0 0 1 0 0 0 1
Infrastructure performance
1 1 1 0 0 0 0 3
Regional freight 2 1 0 0 0 0 0 3
Urban freight 1 0 0 0 0 1 0 2
Mode-specific transport data
2 0 0 1 0 1 1 5
107 | P a g e
Total 6 4 6 8 1 3 1 29
Also, like what was observed for SBEs, when MBEs purchase data, they are happy to pay over $1,000,
as seen in Table 4-42.
Table 4-42. Cross-tabulation between data category sourced externally & cost of access, for MBEs
Cost to access data
Counts Less than $1,000 $1,000 - $9,999 $10,000 or more Total
Dat
a ca
tego
ry
Competitiveness 10 1 0 11
Performance of international
gateways
1 1 0 3
Performance of multimodal
networks
2 0 0 4
Infrastructure performance
3 1 0 3
Regional freight 1 2 0 3
Urban freight 0 1 1 2
Mode-specific transport data
3 0 2 5
Total 20 6 3 29
Table 4-43 shows external data used by LBEs; data uses is fairly evenly distributed across categories.
108 | P a g e
Table 4-43. Cross-tabulation between data category sourced externally & subcategory, LBEs
Data Subcategory
Counts
Po
rts
Air
po
rts
Bes
t P
ract
ice
Mo
del
ling
Ass
um
pti
on
s
Ro
ad
Last
mile
per
form
ance
met
rics
Lan
d s
up
ply
an
d
con
flic
t
Lan
dsi
de
logi
stic
s
cost
s
Rai
l
Fore
cast
ing
and
pro
ject
ion
Oth
er
Tota
l
Dat
a ca
tego
ry
Competitiveness 1 0 0 0 0 0 0 0 0 0 1
Performance of international gateways
0 1 0 1 0 0 0 2 0 0 4
Performance of multimodal networks
0 0 0 0 0 0 1 1 0 0 2
Infrastructure performance
1 0 0 0 0 0 1 0 0 0 2
Safety 0 0 0 0 0 0 0 0 0 1 1
Regional freight 0 0 0 0 1 0 3 0 0 0 4
Urban freight 0 0 0 0 1 1 0 0 0 0 2
Resilient freight 0 1 0 0 0 0 0 0 0 0 1
Mode-specific transport data
1 0 1 2 1 0 0 1 0 0 6
Other 1 0 0 0 0 0 0 0 1 0 2
109 | P a g e
Total 4 2 1 3 3 1 5 4 1 1 25
110 | P a g e
Table 4-44. Cross-tabulation between data category sourced externally & purpose, LBEs
Data Purpose
Counts Planning Operation Investment Total
Dat
a ca
tego
ry
Competitiveness 0 0 1 1
Performance of international gateways
4 0 0 4
Performance of multimodal networks
1 1 0 2
Infrastructure performance 0 1 1 2
Safety 0 1 0 1
Regional freight 2 2 0 4
Urban freight 0 2 0 2
Resilient freight 0 1 0 1
Mode-specific transport data 5 1 0 6
Other 2 0 0 2
Total 14 9 2 25
Table 4-45. Cross-tabulation between data category sourced externally & frequency of use, LBEs
Frequency of use
Counts Every day Once a week Once a month
Every three months
Every year or more
Total
Dat
a ca
tego
ry
Competitiveness 1 0 0 0 0 1
Performance of international gateways
0 1 0 3 0 4
Performance of multimodal networks
1 0 0 1 0 2
Infrastructure performance
0 2 0 0 0 2
Safety 0 0 1 0 0 1
Regional freight 2 0 1 1 0 4
Urban freight 1 0 1 0 0 2
Resilient freight 0 0 0 1 0 1
Mode-specific transport data
4 1 0 0 1 6
Other 0 0 1 1 0 2
Total 9 4 4 7 1 25
111 | P a g e
The cost of data being used by large companies appears not to be not very high, as most of all
observations fall under the category of less than $1,000 (Table 4-46).
Table 4-46. Cross-tabulation between data category sourced externally & cost of access, LBEs
Cost to access data
Counts Less than
$1,000 $1,000 - $9,999
$10,000 or more
Total
Dat
a ca
tego
ry
Competitiveness 1 0 0 1
Performance of international gateways
4 0 0 4
Performance of multimodal networks
2 0 0 2
Infrastructure performance 2 0 0 2
Safety 1 0 0 1
Regional freight 3 0 1 4
Urban freight 1 0 1 2
Resilient freight 1 0 0 1
Mode-specific transport data 4 1 1 6
Other 2 0 0 2
Total 21 1 3 25
The IA respondents use the data for particularly planning purposes (Table 4-47). IA bodies use
externally sourced data less frequently than other companies and are willing to pay less than $1,000
for the data they purse from external sources (Table 4-48).
112 | P a g e
Table 4-47. Cross-tabulation between data category sourced externally & subcategory, IAs
Data Subcategory
Counts
Po
rts
Net
wo
rk
Op
tim
isat
ion
Fram
ewo
rks
Ro
ads,
tra
cks,
bri
dge
s, t
un
ne
ls
Ro
ad
Vo
lum
es
Last
mile
per
form
ance
met
rics
Lan
d s
up
ply
an
d
con
flic
t
Lan
dsi
de
logi
stic
s
cost
s
Co
nge
stio
n m
etr
ics
Mar
ket
com
par
iso
n
Oth
er
Tota
l
Dat
a ca
tego
ry
Competitiveness 1 0 0 0 0 0 1 1 0 0 0 3
Performance of international gateways
3 0 0 0 0 0 0 0 0 0 0 3
Performance of multimodal networks
0 0 0 0 0 0 0 2 0 0 0 2
Infrastructure performance
0 0 1 0 0 0 0 1 0 0 0 2
Safety 0 0 0 2 1 0 1 0 0 0 1 5
Regional freight 0 1 0 0 0 0 0 1 0 0 1 3
Urban freight 0 0 0 0 1 0 0 1 1 0 0 3
Resilient freight 0 0 1 0 0 0 0 0 0 0 0 1
Mode-specific transport data
0 0 0 0 1 1 0 0 0 1 0 3
Other 0 0 0 0 0 0 0 0 0 0 1 1
Total 4 1 2 2 3 1 2 6 1 1 3 26
113 | P a g e
Table 4-48. Cross-tabulation between data category sourced externally & purpose, IAs
Data Purpose
Counts Planning Operation Investment Total
Dat
a ca
tego
ry
Competitiveness 1 1 1 3
Performance of international gateways
1 1 1 3
Performance of multimodal networks
1 0 1 2
Infrastructure performance 1 0 1 2
Safety 2 3 0 5
Regional freight 2 0 1 3
Urban freight 1 1 1 3
Resilient freight 0 0 1 1
Mode-specific transport data 1 0 2 3
Other 0 1 0 1
Total 10 7 9 26
Table 4-49. Cross-tabulation between data category sourced externally & frequency of use, IAs
Frequency of use
Counts Every day
Once a week
Once a month
Every three months
Every year or more
Total
Dat
a ca
tego
ry
Competitiveness 0 0 1 0 2 3
Performance of international gateways
0 1 1 0 1 3
Performance of multimodal networks
0 1 0 0 1 2
Infrastructure performance
0 0 1 0 1 2
Safety 0 0 1 3 1 5
Regional freight 0 0 1 0 2 3
Urban freight 1 0 1 0 1 3
Resilient freight 0 0 0 0 1 1
Mode-specific transport data
0 0 1 0 2 3
Other 0 0 0 0 1 1
114 | P a g e
Frequency of use
Counts Every day
Once a week
Once a month
Every three months
Every year or more
Total
Total 1 2 7 3 13 26
Table 4-50. Cross-tabulation between data category sourced externally & cost of access, IAs
Cost to access data
Counts Less than
$1,000
$10,000 or more
Total
Dat
a ca
tego
ry
Competitiveness 3 0 3
Performance of international gateways
2 1 3
Performance of multimodal networks
2 0 2
Infrastructure performance
2 0 2
Safety 5 0 5
Regional freight 3 0 3
Urban freight 3 0 3
Resilient freight 1 0 1
Mode-specific transport data
3 0 3
Other 1 0 1
Total 25 1 26
A.2.3. Responses to propositions
In this section, we report the results of six propositions that were presented to respondents as part of
a focus group session with the industry stakeholders.
115 | P a g e
Figure 4-19. Responses to the 6 propositions
Respondents could pick more than one proposition. Table 4-51 presents the different combinations
of selection among the respondents. The table shows that 85% of respondents found at least one
proposition to be relevant to their circumstances, 35% of the responses are for those finding more
than one response to be relevant to their cases, while 50% of respondents found only one to be critical
to their interests.
Table 4-51. Different combination of selection of proposition among the respondents
Combination of selection Percent
None of Proposition 13.5%
Proposition 2 12.8%
Proposition 5 12.8%
Proposition 1 10.1%
All Propositions 8.1%
Proposition 6 5.4%
Propositions 2& 3 3.4%
Propositions 1 & 2 & 3 & 4 & 6 3.4%
Proposition 3 2.7%
15%
22%
16%
12%
16%
13%
6%
Proposition One – Bulk Commodities
Proposition Two – Non-Express Domestic Forwarding (FTL, LTL, Rail, Sea)
Proposition Three – Import Containers and National Gateways
Proposition Four – Agricultural Goods
Proposition Five – Express, E-Commerce, Urban First and Last Mile Deliveries
Proposition Six – Land Planning and Corridor Protection
None of the above (As part of this study we will beseeking to make actionable recommendations to
government about which
116 | P a g e
Combination of selection Percent
Propositions 1 & 2 2.7%
Proposition 4 2.0%
Propositions 2& 4 2.0%
Propositions 2 & 3 & 4 2.0%
Propositions 2 & 3 & 4 & 5 & 6 2.0%
Propositions 3 & 5 1.4%
Propositions 1 & 4 & 5 1.4%
Proposition 2 & 3 & 5 1.4%
Propositions 2 & 5 & 6 1.4%
Propositions 1 & 2 & 3 & 4 1.4%
Propositions 2 & 3 & 5 & 6 1.4%
Propositions 1 & 3 0.7%
Propositions 1 & 4 0.7%
Propositions 2 & 5 0.7%
Propositions 1 & 3 & 6 0.7%
Propositions 2 & 3 & 6 0.7%
Propositions 4 & 5 & 6 0.7%
Propositions 1 & 2 & 3 & 6 0.7%
Propositions 1 & 3 & 5 & 6 0.7%
Propositions 2 & 3 & 4 & 5 0.7%
Propositions 2 & 3 & 4 & 6 0.7%
Propositions 3 & 4 & 5 & 6 0.7%
Propositions 1 & 2 & 3 & 5 & 6 0.7%
Propositions 2 & 3 & 4 & 5 & 6 0.7%
68.5% of the respondents found the existing data sources sufficient for their needs.
Figure 4-20. Are there any gaps in the currently available data sources required for your entity?
To further understand which types of entities expressed further needs for accessibility to more data,
Table 4-52 breaks down which entity believes there are gaps in the currently existing data. SBEs and
MBEs are reasonably satisfied with the available data sources, while LBEs and IAs requested for more
data sources to become available to them.
117 | P a g e
Table 4-52. Cross-tabulation between the type of entity and if there are any gaps in the currently available data sources required for your entity
Are there any gaps in the currently available data
sources required for your entity?
Total
Yes No
Wh
at
so
rt o
f e
nti
ty a
re
yo
u r
es
po
nd
ing
on
be
ha
lf o
f?
Small Business 19 48 67
Medium Business 12 25 37
Large Business 14 11 25
Industry Association 7 3 10
Other 7 2 9
Total 59 89 148
Furthermore, by looking at the type of data entities consider as a gap, entities are less concerned
about gaps in the following data categories: safety, regional freight, urban freight and mode specific
transport. However, more data should be provided on performance of international gateways,
competitiveness, performance of multimodal networks, Infrastructure performance and regional
freight (Table 4-53).
Table 4-53. Cross-tabulation between data category in demand and if there are any gaps in the currently available data sources required for your entity
Data category (Missing data) To
tal
Co
mp
etit
iven
ess
Per
form
ance
of
inte
rnat
ion
al
gate
way
s
Per
form
ance
of
mu
ltim
od
al
net
wo
rks
Infr
astr
uct
ure
per
form
ance
Safe
ty
Reg
ion
al f
reig
ht
Urb
an f
reig
ht
Res
ilie
nt
frei
ght
Mo
de-
spec
ific
tran
spo
rt d
ata
Oth
er
Are
th
ere
any
gap
s in
th
e cu
rren
tly
avai
lab
le d
ata
sou
rces
req
uir
ed f
or
you
r en
tity
?
Yes 14 12 21 13 2 15 5 1 12 2 97
Among the subcategories of data, landside logistics costs are those identified by the respondents
requiring further supporting data sources (Table 4-54).
118 | P a g e
Table 4-54. Cross-tabulation between data sub-category in demand and if there are any gaps in the currently available data sources required for your entity
Subcategory (Missing data)
Lab
ou
r
Val
ue
of
frei
ght
to t
he
nat
ion
al
eco
no
my
Po
rts
Air
po
rts
Cu
sto
ms
Frei
ght
Dat
a A
nal
ysis
Pro
ject
Net
wo
rk O
pti
mis
atio
n F
ram
ewo
rks
Bes
t P
ract
ice
Mo
del
ling
Ass
um
pti
on
s
Ro
ads,
tra
cks,
bri
dge
s, t
un
nel
s
Ro
ad
Vo
lum
es
Firs
t m
ile a
cces
s
Last
mile
per
form
ance
me
tric
s
Lan
d s
up
ply
an
d c
on
flic
t
Lan
dsi
de
logi
stic
s co
sts
Co
nge
stio
n m
etr
ics
Rem
ote
met
rics
fo
r N
ort
her
n A
ust
ralia
Rai
l
Fore
cast
ing
and
pro
ject
ion
Tim
esta
mp
Mar
ket
com
par
iso
n
Oth
er
Tota
l
Are
th
ere
an
y ga
ps
in t
he
cu
rre
ntl
y av
aila
ble
dat
a so
urc
es
req
uir
ed
fo
r yo
ur
en
tity
?
Yes 2 7 7 9 8 3 4 2 3 9 1 5 1 13 3 1 4 9 2 2 2 97
The way data is used by the entities is another factor found to be critical in determining whether a gap
is felt by the respondents. Entities are demanding for more data for planning purposes to be available
(Table 4-55).
119 | P a g e
Table 4-55. Cross-tabulation between purpose of data in demand and if there are any gaps in the currently available data sources required for your entity
Purpose of data (Missing data)
Total Planning Operation Investment
Are
th
ere
an
y ga
ps
in
the
cu
rre
ntl
y av
aila
ble
dat
a so
urc
es
req
uir
ed
fo
r yo
ur
en
tity
?
Yes 54 33 10 97
Concerns regarding data gaps and the response to different propositions are evenly distributed
(Table 4-56).
Table 4-56. Cross-tabulation between if there are any gaps in the currently available data sources required for your entity & the six propositions
Propositions Count Percent
Are
th
ere
an
y g
ap
s i
n t
he
cu
rren
tly
av
aila
ble
da
ta s
ou
rce
s
req
uir
ed
fo
r y
ou
r e
nti
ty?
Proposition 1 49 18%
Proposition 2 46 17%
Proposition 3 52 19%
Proposition 4 51 18%
Proposition 5 33 12%
Proposition 6 46 17%
Total 277 100%
Table 4-57 provides insights on data categories identified to be requiring supplementary data and the
propositions selected by the respondents. When competitiveness data types are of interest, the fifth
proposition is again of less importance. The next three data categories that are related to performance
indicators appear to be having a similar pattern of significance across different propositions. The rest
of the categories are not selected frequently by the respondents to require supplementary data,
except for the regional freight data category where propositions 1, 2 and 4 appear not to be quite
attractive.
120 | P a g e
Table 4-57. Cross-tabulation of data categories in demand and the six propositions
Counts
Propositions
1 -Bulk Commodities
2 -Non-Express Domestic Forwarding
3 -Import Containers and National Gateways
4 -Agricultural Goods
5 -Express, E-commerce, Urban First and Last Mile Deliveries
6 -Land Planning and Corridor Protection
Total
Competitiveness 9 7 7 6 4 6 39
Performance of international gateways
4 2 2 4 1 1 14
Performance of multimodal networks
9 9 13 10 6 10 57
Infrastructure performance 6 8 8 7 6 8 43
Safety 1 1 0 1 2 1 6
Regional freight 9 6 8 12 2 8 45
Urban freight 1 2 3 1 3 3 13
Resilient freight 1 1 0 1 1 1 5
Mode-specific transport data 9 9 10 8 7 7 50
Other 0 1 1 1 1 1 5
Total 49 46 52 51 33 46 277
A.3. Limitation & barriers to sharing freight data
This component starts with a Likert scale question to analyse participants understanding of the
importance of 13 transportation factors in moving freight more efficiently. Respondents were asked
to rate each statement from very important to not at all important. Figure 4-21 presents the
percentage for each scale. We found that Transportation cost had the highest percentage selected as
being a very important factor and Knowledge of freight volume had the lowest percentage.
Interestingly, only 24.7% of the respondent had indicated that accessibility to reliable, consistent,
comprehensive and timely data on freight movements is very important.
121 | P a g e
Figure 4-21. How important are the following transportation factors in moving freight more efficiently?
54.7%
49.3%
43.2%
37.2%
33.8%
33.8%
33.8%
31.1%
31.1%
31.1%
31.1%
28.4%
26.4%
25.0%
31.1%
31.8%
27.0%
39.2%
35.8%
29.1%
42.6%
33.1%
31.8%
31.8%
37.8%
40.5%
15.5%
13.5%
18.2%
24.3%
15.5%
20.3%
26.4%
16.2%
27.0%
25.0%
25.0%
24.3%
20.9%
7.4%
7.4%
6.1%
5.4%
6.8%
5.4%
8.1%
8.1%
5.4%
8.8%
2.7%
2.7%
2.7%
4.1%
4.1%
4.1%
5.4%
3.4%
3.4%
4.1%
4.1%
4.1%
3.4%
Transportation cost
Reliability/on-time delivery
Infrastructure condition
Institutional bottlenecks
Access to needed modes
Regulatory cost and an increase in regulations
Safety and security
Accessibility to reliable, consistent, comprehensiveand timely data on freight movements
Cooperation of the public/private sector
Direct/indirect cost of congestion
Capacity bottlenecks
Knowledge of freight type
knowledge of freight volume
Very important Important Neutral Not important Not at all important
122 | P a g e
Figure 4-22 highlights that competition barriers (41.1%) is seen as the most important critical barrier
and challenge for freight data sharing. After that resource barriers with 23.3% was selected as the
second most important barrier.
Figure 4-22. In your opinion, which of the following items is the most important barrier and challenge for freight data sharing?
Based on the literature review the five categories mentioned in Figure 4-22 were further classified
into 20 sub categories (Table 4-58). In order to understand the importance of these factors a best-
worst methodology was used. Best-worst scaling is a type of discrete choice experiment.
14.2%
29.7%
34.5%
4.7%
13.5%
3.4%
Legal Barriers: barriers related to legal andcontractual issues
Resource Barriers: barriers related to lackof time, financial, and human resources
Competition Barriers: barriers related tosensitive data and competitors
Institutional Barriers: barriers related todata governance
Coordination Barriers: barriers related toconsistencies and lack of cooperation
Other
123 | P a g e
Table 4-58. Important categories and sub-categories considered as a barrier for data sharing
Legal Barriers Resource Barriers Competition
Barriers Institutional Barriers
Coordination Barriers
Lack of a formal contract
Small companies find it harder to provide
freight data
Sensitivity about sharing
information which could be used by
competitors
Lengthy negotiation process to obtain approval for data
sharing; extra time needs to be planned
Not articulating uses of data to
private data providers
Lack of legal basis for public-
private partnerships
Lack of financial subsidies for data
sharing make it difficult to keep all
partners interested in and committed to
participation
Disclosure of individual
shipment or company data is
viewed as proprietary or
business-sensitive
Private sector interests do not
always align with the public good
Lack of coordination
with stakeholders
Control of data by technology
contractor
Limitations in data analysis that can be
done with aggregated data
Increased requirements of data compliance may delay cargo
Different facilities, such as border
crossings operate differently and may
have different requirements
Sharing across international boundaries is difficult as is coordination with multiple international
agencies
National security
sensitivities
Data source diversity, and in some cases the large amount of data
requires costly processing
Third-party data supplier’ s
validation and cleaning process
not known
Compatibility issues between national freight data sets
Data sharing with foreign
countries
Table 4-59and Table 4-60 report the ranking of the studied factors based on industry segmentation
(being shippers, receivers, providers, carriers). In the first column in
124 | P a g e
Table 4-59 we have presented the ranking of the factors for the full sample. In Table 4-60 we have
segmented the data based on entity types (being SBEs, MBEs and LBEs). The results of this analysis are
similar to the results classified by industry group.
125 | P a g e
Table 4-59. Ranking of most to least important factor that participants (based on their role in the freight chain supply) consider as a barrier to sharing freight data
Factors
All sample (ranking)
N=148
Shippers (ranking)
N=100
Receivers (ranking)
N=95
Providers (ranking)
N=104
Carriers (ranking)
N=70
Sensitivity about sharing information which could be used by competitors
1 1 1 1 2
Disclosure of individual shipment or company data is viewed as proprietary or business-sensitive
2 2 1 1 1
Data source diversity, and in some cases the large amounts of data requires costly processing
3 3 2 2 3
Limitations in data analysis that can be done with aggregated data
4 11 6 3 9
Third-party data supplier's validation and cleaning process not known
4 7 5 3 9
Compatibility issues between national freight data sets 5 6 5 5 9
Private sector interests do not always align with the public good
5 12 5 3 7
Sharing across international boundaries is difficult as is coordination with multiple international agencies
6 5 4 4 5
Increased requirements of data compliance may delay cargo
6 4 3 3 4
Lack of coordination with stakeholders 7 10 6 5 7
Lack of financial subsidies for data sharing make it difficult to keep all partners interested in and committed to participation
7 8 4 6 8
Not articulating uses of data to private data providers 8 15 8 5 8
Small companies find it harder to provide freight data 8 13 6 6 7
Different facilities, such as border crossings operate differently and may have different requirements
9 14 8 8 10
Lack of legal basis for public-private partnerships 10 9 7 7 11
Lengthy negotiation process to obtain approval for data sharing; extra time needs to be planned
11 16 8 9 10
Control of data by technology contractor 11 17 9 6 9
National security sensitivities 12 18 6 7 6
Lack of a formal contract 13 19 10 10 12
Data sharing with foreign countries 14 20 11 11 13
Competition Barriers Coordination Barriers Legal Barriers
Resource Barriers Institutional Barriers
126 | P a g e
Table 4-60. Ranking of most to least important factors that participants (based on their entity size) had consider as a barrier to sharing freight data
Factors All sample (ranking)
N=148
Small business (ranking)
N=67
Medium business (ranking)
N=37
Large business (ranking)
N=25
Industry Association
(ranking) N=10
Disclosure of individual shipment or company data is viewed as proprietary or business-sensitive
1 1 4 2 2
Sensitivity about sharing information which could be used by competitors
2 7 1 1 1
Lack of financial subsidies for data sharing make it difficult to keep all partners interested in and committed to participation
3 2 12 11 7
Limitations in data analysis that can be done with aggregated data
3 5 8 5 5
Sharing across international boundaries is difficult as is coordination with multiple international agencies
3 7 3 7 5
Data source diversity, and in some cases the large amounts of data requires costly processing
4 3 6 3 6
Third-party data supplier's validation and cleaning process not known
4 5 5 6 7
Compatibility issues between national freight data sets 4 10 2 5 3
Private sector interests do not always align with the public good
5 7 10 4 3
Lack of legal basis for public-private partnerships 6 9 7 10 9
Not articulating uses of data to private data providers 7 8 9 9 4
Lack of coordination with stakeholders 8 11 8 5 7
Lengthy negotiation process to obtain approval for data sharing; extra time needs to be planned
9 10 11 8 8
Small companies find it harder to provide freight data 10 6 7 10 8
Increased requirements of data compliance may delay cargo
10 4 11 6 6
National security sensitivities 10 9 11 13 11
Different facilities, such as border crossings operate differently and may have different requirements
11 7 9 7 9
Control of data by technology contractor 11 6 10 9 10
Lack of a formal contract 12 12 13 14 6
Data sharing with foreign countries 13 13 9 12 12
Almost one-third of the sampled participants had indicated that they are currently involved in any
existing cooperation between Australian data holders. Table 4-61 represents a cross-tabulation
between the type of entity and if their entity is currently involved in any existing cooperation between
Australian data holders.
Competition Barriers Coordination Barriers Legal Barriers
Resource Barriers Institutional Barriers
127 | P a g e
Table 4-61. Cross-tabulation between the type of entity and if their entity is currently involved in any existing cooperation between Australian data holders
Count
Is your entity currently involved in any existing cooperation between Australian data holders?
Yes No Total
Wh
at
so
rt o
f e
nti
ty a
re y
ou
res
po
nd
ing
on
be
ha
lf o
f?
Small business 18 49 67
Medium business 14 23 37
Large business 8 17 25
Industry Association 4 6 10
Other 1 8 9
Total 45 103 148
128 | P a g e
Appendix B. Best-worst scores
129 | P a g e
Figure 4-23. Best Worst scores for all sample (n=148)
0.16
0.14
0.06
0.03
0.03
0.02
0.02
0.01
0.01
0.00
-0.00
-0.01
-0.01
-0.02
-0.03
-0.04
-0.04
-0.07
-0.09
-0.15
Sensitivity about sharing information which could be usedby competitors
Disclosure of individual shipment or company data isviewed as proprietary or business-sensitive
Data source diversity, and in some cases the largeamounts of data requires costly processing
Limitations in data analysis that can be done withaggregated data
Third-party data supplier's validation and cleaning processnot known
Compatibility issues between national freight data sets
Private sector interests do not always align with the publicgood
Sharing across international boundaries is difficult as iscoordination with multiple international agencies
Increased requirements of data compliance may delaycargo
Lack of coordination with stakeholders
Lack of financial subsidies for data sharing make it difficultto keep all partners interested in and committed to…
Not articulating uses of data to private data providers
Small companies find it harder to provide freight data
Different facilities, such as border crossings operatedifferently and may have different requirements
Lack of legal basis for public-private partnerships
Lengthy negotiation process to obtain approval for datasharing; extra time needs to be planned
Control of data by technology contractor
National security sensitivities
Lack of a formal contract
Data sharing with foreign countries
130 | P a g e
Figure 4-24. Best-Worst Scores for Shippers (n=100)
131 | P a g e
0.11
0.11
0.06
0.04
0.03
0.03
0.02
0.02
0.00
0.00
-0.00
-0.01
-0.01
-0.02
-0.02
-0.03
-0.04
-0.05
-0.11
-0.12
Sensitivity about sharing information which could be used bycompetitors
Disclosure of individual shipment or company data is viewedas proprietary or business-sensitive
Data source diversity, and in some cases the large amountsof data requires costly processing
Increased requirements of data compliance may delay cargo
Sharing across international boundaries is difficult as iscoordination with multiple international agencies
Compatibility issues between national freight data sets
Third-party data supplier's validation and cleaning processnot known
Lack of financial subsidies for data sharing make it difficult tokeep all partners interested in and committed to…
Lack of legal basis for public-private partnerships
Lack of coordination with stakeholders
Limitations in data analysis that can be done withaggregated data
Private sector interests do not always align with the publicgood
Small companies find it harder to provide freight data
Different facilities, such as border crossings operatedifferently and may have different requirements
Not articulating uses of data to private data providers
Lengthy negotiation process to obtain approval for datasharing; extra time needs to be planned
Control of data by technology contractor
National security sensitivities
Lack of a formal contract
Data sharing with foreign countries
132 | P a g e
Figure 4-25. Best-Worst Scores for Receivers (n=95)
133 | P a g e
0.06
0.06
0.05
0.04
0.02
0.02
0.01
0.01
0.01
0.00
0.00
0.00
0.00
0.00
-0.02
-0.02
-0.02
-0.03
-0.08
-0.12
Sensitivity about sharing information which could be usedby competitors
Disclosure of individual shipment or company data isviewed as proprietary or business-sensitive
Data source diversity, and in some cases the large amountsof data requires costly processing
Increased requirements of data compliance may delaycargo
Sharing across international boundaries is difficult as iscoordination with multiple international agencies
Lack of financial subsidies for data sharing make it difficultto keep all partners interested in and committed to…
Third-party data supplier's validation and cleaning processnot known
Private sector interests do not always align with the publicgood
Compatibility issues between national freight data sets
National security sensitivities
Small companies find it harder to provide freight data
Limitations in data analysis that can be done withaggregated data
Lack of coordination with stakeholders
Lack of legal basis for public-private partnerships
Lengthy negotiation process to obtain approval for datasharing; extra time needs to be planned
Not articulating uses of data to private data providers
Different facilities, such as border crossings operatedifferently and may have different requirements
Control of data by technology contractor
Lack of a formal contract
Data sharing with foreign countries
134 | P a g e
Figure 4-26. Best-Worst Scores for Providers (n=104)
135 | P a g e
0.10
0.10
0.04
0.02
0.02
0.02
0.02
0.01
0.00
0.00
0.00
-0.01
-0.01
-0.01
-0.02
-0.02
-0.03
-0.04
-0.10
-0.11
Sensitivity about sharing information which could be used bycompetitors
Disclosure of individual shipment or company data is viewed asproprietary or business-sensitive
Data source diversity, and in some cases the large amounts ofdata requires costly processing
Private sector interests do not always align with the publicgood
Limitations in data analysis that can be done with aggregateddata
Increased requirements of data compliance may delay cargo
Third-party data supplier's validation and cleaning process notknown
Sharing across international boundaries is difficult as iscoordination with multiple international agencies
Compatibility issues between national freight data sets
Not articulating uses of data to private data providers
Lack of coordination with stakeholders
Control of data by technology contractor
Small companies find it harder to provide freight data
Lack of financial subsidies for data sharing make it difficult tokeep all partners interested in and committed to participation
Lack of legal basis for public-private partnerships
National security sensitivities
Different facilities, such as border crossings operate differentlyand may have different requirements
Lengthy negotiation process to obtain approval for datasharing; extra time needs to be planned
Lack of a formal contract
Data sharing with foreign countries
136 | P a g e
Figure 4-27. Best-Worst Scores for Carriers (n=70)
137 | P a g e
0.13
0.12
0.03
0.02
0.01
0.00
0.00
0.00
0.00
-0.00
-0.00
-0.01
-0.01
-0.01
-0.01
-0.02
-0.02
-0.04
-0.05
-0.12
Disclosure of individual shipment or company data is viewed asproprietary or business-sensitive
Sensitivity about sharing information which could be used bycompetitors
Data source diversity, and in some cases the large amounts ofdata requires costly processing
Increased requirements of data compliance may delay cargo
Sharing across international boundaries is difficult as iscoordination with multiple international agencies
National security sensitivities
Small companies find it harder to provide freight data
Private sector interests do not always align with the publicgood
Lack of coordination with stakeholders
Lack of financial subsidies for data sharing make it difficult tokeep all partners interested in and committed to participation
Not articulating uses of data to private data providers
Control of data by technology contractor
Limitations in data analysis that can be done with aggregateddata
Third-party data supplier's validation and cleaning process notknown
Compatibility issues between national freight data sets
Lengthy negotiation process to obtain approval for datasharing; extra time needs to be planned
Different facilities, such as border crossings operate differentlyand may have different requirements
Lack of legal basis for public-private partnerships
Lack of a formal contract
Data sharing with foreign countries
138 | P a g e
Figure 4-28. Best-Worst Scores for Small Business Entities (n=67)
139 | P a g e
0.08
0.07
0.06
0.05
0.02
0.02
0.01
0.01
0.00
0.00
0.00
0.00
0.00
-0.00
-0.00
-0.03
-0.03
-0.04
-0.07
-0.17
Disclosure of individual shipment or company data is viewedas proprietary or business-sensitive
Lack of financial subsidies for data sharing make it difficult tokeep all partners interested in and committed to participation
Data source diversity, and in some cases the large amounts ofdata requires costly processing
Increased requirements of data compliance may delay cargo
Limitations in data analysis that can be done with aggregateddata
Third-party data supplier's validation and cleaning process notknown
Small companies find it harder to provide freight data
Control of data by technology contractor
Sensitivity about sharing information which could be used bycompetitors
Private sector interests do not always align with the publicgood
Different facilities, such as border crossings operatedifferently and may have different requirements
Sharing across international boundaries is difficult as iscoordination with multiple international agencies
Not articulating uses of data to private data providers
Lack of legal basis for public-private partnerships
National security sensitivities
Lengthy negotiation process to obtain approval for datasharing; extra time needs to be planned
Compatibility issues between national freight data sets
Lack of coordination with stakeholders
Lack of a formal contract
Data sharing with foreign countries
140 | P a g e
Figure 4-29. Best-Worst Scores for Medium Business Entities (n=37)
141 | P a g e
0.11
0.10
0.07
0.06
0.05
0.02
0.01
0.01
0.00
0.00
-0.02
-0.02
-0.02
-0.04
-0.04
-0.05
-0.05
-0.05
-0.07
-0.08
Sensitivity about sharing information which could be used bycompetitors
Compatibility issues between national freight data sets
Sharing across international boundaries is difficult as iscoordination with multiple international agencies
Disclosure of individual shipment or company data is viewed asproprietary or business-sensitive
Third-party data supplier's validation and cleaning process notknown
Data source diversity, and in some cases the large amounts ofdata requires costly processing
Lack of legal basis for public-private partnerships
Small companies find it harder to provide freight data
Limitations in data analysis that can be done with aggregateddata
Lack of coordination with stakeholders
Data sharing with foreign countries
Different facilities, such as border crossings operate differentlyand may have different requirements
Not articulating uses of data to private data providers
Control of data by technology contractor
Private sector interests do not always align with the publicgood
National security sensitivities
Lengthy negotiation process to obtain approval for datasharing; extra time needs to be planned
Increased requirements of data compliance may delay cargo
Lack of financial subsidies for data sharing make it difficult tokeep all partners interested in and committed to participation
Lack of a formal contract
142 | P a g e
Figure 4-30. Best-Worst Scores for Large Business Entities (n=25)
0.43
0.35
0.07
0.05
0.04
0.04
0.04
0.00
0.00
-0.03
-0.03
-0.04
-0.08
-0.08
-0.09
-0.09
-0.11
-0.12
-0.16
-0.19
Sensitivity about sharing information which could beused by competitors
Disclosure of individual shipment or company data isviewed as proprietary or business-sensitive
Data source diversity, and in some cases the largeamounts of data requires costly processing
Private sector interests do not always align with thepublic good
Limitations in data analysis that can be done withaggregated data
Compatibility issues between national freight datasets
Lack of coordination with stakeholders
Increased requirements of data compliance maydelay cargo
Third-party data supplier's validation and cleaningprocess not known
Different facilities, such as border crossings operatedifferently and may have different requirements
Sharing across international boundaries is difficult asis coordination with multiple international agencies
Lengthy negotiation process to obtain approval fordata sharing; extra time needs to be planned
Control of data by technology contractor
Not articulating uses of data to private dataproviders
Lack of legal basis for public-private partnerships
Small companies find it harder to provide freightdata
Lack of financial subsidies for data sharing make itdifficult to keep all partners interested in and…
Data sharing with foreign countries
National security sensitivities
Lack of a formal contract
143 | P a g e
Figure 4-31. Best-Worst Scores for Industry Association (n=10)
0.50
0.33
0.13
0.13
0.10
0.07
0.07
0.00
0.00
0.00
-0.03
-0.03
-0.03
-0.07
-0.07
-0.13
-0.13
-0.17
-0.23
-0.43
Sensitivity about sharing information which could beused by competitors
Disclosure of individual shipment or company data isviewed as proprietary or business-sensitive
Private sector interests do not always align with thepublic good
Compatibility issues between national freight datasets
Not articulating uses of data to private dataproviders
Limitations in data analysis that can be done withaggregated data
Sharing across international boundaries is difficult asis coordination with multiple international agencies
Lack of a formal contract
Data source diversity, and in some cases the largeamounts of data requires costly processing
Increased requirements of data compliance maydelay cargo
Lack of financial subsidies for data sharing make itdifficult to keep all partners interested in and…
Third-party data supplier's validation and cleaningprocess not known
Lack of coordination with stakeholders
Small companies find it harder to provide freightdata
Lengthy negotiation process to obtain approval fordata sharing; extra time needs to be planned
Lack of legal basis for public-private partnerships
Different facilities, such as border crossings operatedifferently and may have different requirements
Control of data by technology contractor
National security sensitivities
Data sharing with foreign countries
144 | P a g e
Appendix C. Survey instrument
145 | P a g e
146 | P a g e
147 | P a g e
148 | P a g e
149 | P a g e
150 | P a g e
151 | P a g e
152 | P a g e
153 | P a g e
154 | P a g e
155 | P a g e
156 | P a g e
157 | P a g e
